Senior AI Engineer

🏢 Summary: Product Manager role owning a core product area focused on building infrastructure for industrial AI. The position bridges complex technical systems, AI capabilities, and customer workflows to define strategy, shape features, and deliver high-impact solutions. It involves close collaboration with engineering and design teams in a fully remote, fast-paced environment. 🗂️ Requirements: 5-8+ years in Product Management or Product Ownership, Proven experience defining product vision and roadmaps, Hands-on knowledge of LLMs and agentic systems, Experience building AI-related prototypes or projects, Experience with Agile methodologies (Scrum or Kanban), Ability to create user stories, specifications, acceptance criteria, Experience managing product backlogs and sprint planning, Strong stakeholder management skills, Ability to define and track KPIs, Experience working with engineering teams on technical trade-offs, Fluent English (C1 level) 📃 Skills: AI, LLM, Agile, Scrum, Kanban, KPIs, APIs, UX, Analytics, ProductManagement, AgenticSystems 🏢 Description: About Cerebre cerebre is on a mission to build the data foundation of the physical world. We offer a live intelligence map of a facilities – built from schematics to power AI. Often called the “brain” of the digital twin, cerebre helps engineers, data scientists, and business teams understand data within the context of the plant. We collaborate with the world’s largest companies and most innovative partners who are transforming the industrial sector. cerebre is a global team of engineers, scientists, innovators, and explorers united by a mission to help the world’s largest manufacturing facilities build and use intelligence. Our development team is made up of world-class engineers who design and deliver novel solutions. This is an opportunity to join a market-leading team that is changing how the industrial world works. If you love building and creating value in the “white space,” if you thrive with the freedom and flexibility to think outside the box, if you are passionate about working with critical thinkers who challenge the status quo, and if you aspire to work in a fast-paced environment – we would love to meet you! We believe flexibility drives creativity and that our team should live and work where we can be our best selves. We’re 100% remote and offer a competitive time-off package to ensure time for rest and recharge. About the role We're looking for a Product Manager to own a core product area at Cerebre. We're building infrastructure for industrial AI, and this role sits at the intersection of complex technical systems, customer-facing workflows, and emerging AI capabilities. You will work closely with our engineers, designers, and clients to shape product strategy, define features, and ensure timely delivery of high-impact solutions. Key Responsibilities Product Vision & Roadmap: Define and communicate direction for your product area, aligned with Cerebre's broader strategy. Backlog Management: Own, prioritize, and refine the product backlog to ensure that development efforts focus on the highest-value items. Stakeholder Alignment: Partner with leadership, customers, and cross-functional teams to ensure the product reflects real needs and strategic intent. Translate strategy into execution: Turn outcomes and hypotheses into clear user stories, specs, and acceptance criteria. Sprint Planning & Execution: Partner with engineering and design teams to plan sprints, ensure clarity on deliverables, and resolve blockers. Market & User Insights: Continuously pull & distill signal from customers, partners, and the broader industrial AI landscape to inform decisions. Performance Tracking: Define and monitor KPIs to measure product success and inform future improvements. Process Improvement: Help evolve Agile practices and product workflows to increase efficiency and effectiveness. Customer Engagement: Support customer conversations, demos, and feedback sessions. Required Skills 5-8+ years of professional experience in product ownership or product management, with a track record of defining vision, building roadmaps, and shipping. Working knowledge of AI and agentic systems: You understand how LLMs work, what agentic workflows are, and where they break. You've built something yourself in this space (a prototype, personal side project, hands-on engagement, etc.) & can speak credibly about it. Self-starter with sharp instincts for the right questions: You don't wait to be told what matters. You can walk into ambiguity, figure out what's going on, and ask the questions that surface the real problem. Strong communication skills: Ability to translate technical concepts into business terms and business needs into clear product requirements. Experience with Agile methodologies: Familiarity with Scrum or Kanban, including backlog grooming, sprint planning, and retrospectives. Stakeholder management: Proven ability to balance the needs of customers, internal teams, and leadership, ensuring alignment on priorities. Analytical thinking: Skilled at gathering insights from data, feedback, and market trends to inform product decisions. User-centric mindset: Experience defining features that create intuitive, valuable user experiences. Technical familiarity: Comfort working with engineering teams and understanding technical trade-offs. Strong organizational skills: Ability to manage multiple priorities in a fast-paced, remote environment. At least C1 English level: Strong command of English for effective collaboration in a global team. Preferred Skills Previous experience in a high-growth startup environment. Previous experience being an initial PM for an application or new product surface area. Familiarity with industrial data, engineering software, or digital twins a plus Knowledge of UX/UI principles to support feature design and user flows. Experience building API products or developer-facing platforms a plus Background in data-driven product development and analytics. Certification as a Certified Scrum Product Owner (CSPO) or equivalent. More about Cerebre We are cross-functional collaborators. We blend manufacturing process knowledge with software and big data engineering expertise to create value in physical settings We are experienced. We are armed with industry-leading experts in numerical simulation, combustion, power, computational fluid dynamics, and chemical process modeling We are serious builders. We develop our platforms using leading practices in IT/OT architecture, OT security, AI architecture, ML Ops, and Platform engineering

🏢 Summary: Senior Engineer role focused on owning and evolving the query and compiler pipeline, translating complex queries into efficient, scalable execution plans. The position drives advanced optimization strategies across AST transformations and execution planning while ensuring strict correctness of query semantics. The role directly impacts system performance, scalability, and database engine reliability in a graph-oriented environment. 🗂️ Requirements: Expert-level experience in C# and .NET backend development, Strong database design experience, Proven query performance optimization experience, Experience designing and tuning database indexes, Experience with graph databases or relationship-heavy data models, Ability to analyze and optimize query execution plans, Experience profiling backend or database-heavy production systems, Strong knowledge of data structures and algorithms for database workloads, Experience maintaining database stability, replication, and sharding 📃 Skills: C#, .NET, SQL, Cypher, Gremlin, SPARQL, ANTLR, Neo4j, Memgraph, FalkorDB, RedisGraph, PostgreSQL, SQLServer, AST, Compilers, Profiling, Sharding, Replication, Indexing, Algorithms 🏢 Description: About Cerebre Cerebre is on a mission to build the data foundation of the physical world. We offer a live intelligence map of facilities, built from schematics to power AI. Often called the “brain” of the digital twin, cerebre helps engineers, data scientists, and business teams understand data within the context of the plant. We collaborate with the world’s largest companies and most innovative partners who are transforming the industrial sector. Cerebre is a global team of engineers, scientists, innovators, and explorers united by a mission to help the world’s largest manufacturing facilities build and use intelligence. Our development team is made up of world-class engineers who design and deliver novel solutions. This is an opportunity to join a market-leading team that is changing how the industrial world works. If you love building and creating value in the “white space,” if you thrive with the freedom and flexibility to think outside the box, if you are passionate about working with critical thinkers who challenge the status quo, and if you aspire to work in a fast-paced environment, then we would love to meet you! We believe flexibility drives creativity and that our team should live and work where we can be our best selves. We’re 100% remote and offer a competitive time-off package to ensure time for rest and recharge. About the role We are seeking a Senior Engineer to take ownership of critical components of our query and compilation systems. In this role, you will drive the design and evolution of the compiler pipeline, from parsing and AST construction through validation, optimization, and execution-plan generation, ensuring that complex queries are translated into efficient, scalable, and reliable operations across the platform. A core responsibility of this role is leading the design and implementation of advanced optimization strategies. You will develop and refine transformation pipelines, including rewrite rules and optimization passes, that significantly improve performance while maintaining strict correctness of query semantics. This includes optimizing how data is traversed, filtered, aggregated, and executed, with a strong awareness of backend behavior and system constraints. As a senior member of the team, you will operate with a high degree of ownership in ambiguous and complex problem spaces, shaping both system architecture and technical direction. You will collaborate closely with engineers across the platform to ensure that optimizations are robust, maintainable, and aligned with real-world usage patterns. Your work will directly influence system performance, scalability, and the overall developer and user experience. Key Responsibilities Maintain and extend the compiler pipeline, including grammar, AST construction, validation, compilation, and execution-plan generation. Design and implement optimization passes over the AST and execution plan, such as simplification, predicate pushdown, filter combination, dead-step elimination, repetition/path rewrites, and plan normalization. Improve query performance by identifying inefficiencies in traversal, filtering, projection, aggregation, and backend interaction. Preserve correctness of query semantics while introducing compiler optimizations and refactors. Maintain and extend the database engine in terms of stability, replication, sharding, and observability. Required Skills Expert-level C# / .NET backend engineering. Strong database design experience. Strong query performance optimization experience. Strong experience designing and tuning database indexes. Experience with graph databases, knowledge graphs, or relationship-heavy data models. Ability to reason about query execution plans, data access patterns, and performance tradeoffs. Experience profiling backend/database-heavy systems in production. Strong understanding of data structures and algorithms as applied to database/query workloads. Preferred Skills Experience building query engines, query planners, interpreters, rule engines, or DSLs. Experience with graph query languages such as Cypher, Gremlin, SPARQL, or similar. Experience with query rewrite techniques such as predicate pushdown, plan normalization, filter combination, or traversal simplification. Experience with parser generators such as ANTLR, or hand-written parsers. Experience with graph-specific operations such as neighbor traversal, pathfinding, subgraph matching, or recursive traversals. Experience with FalkorDB, Neo4j, Memgraph, RedisGraph, SQL Server, PostgreSQL, or similar database systems. More about Cerebre We are cross-functional collaborators. We blend manufacturing process knowledge with software and big data engineering expertise to create value in physical settings We are experienced. We are armed with industry-leading experts in numerical simulation, combustion, power, computational fluid dynamics, and chemical process modeling We are serious builders. We develop our platforms using leading practices in IT/OT architecture, OT security, AI architecture, ML Ops, and Platform engineering

🏢 Summary: Senior Engineer role focused on owning and evolving a query compiler pipeline, from parsing and AST construction to optimization and execution-plan generation. The position centers on designing advanced optimization passes and rewrite rules to ensure efficient, scalable, and semantically correct query execution. The engineer will shape system architecture and improve performance across traversal, filtering, aggregation, and backend interactions. 🗂️ Requirements: Strong professional experience in C# and .NET, Experience maintaining compilers, interpreters, query planners, or rule engines, Solid understanding of parsing, ASTs, semantic validation, and query generation, Experience designing optimization passes or rewrite rules with semantic correctness, Ability to reason about execution plans and backend-aware performance tradeoffs, Proficiency in English 📃 Skills: C#, .NET, ANTLR, gRPC, ASP.NET, Parsing, AST, Compilers, Optimization, Profiling, Graph, Relational 🏢 Description: About cerebre 👇 Cerebre is on a mission to build the data foundation of the physical world. We offer a live intelligence map of facilities, built from schematics to power AI. Often called the “brain” of the digital twin, cerebre helps engineers, data scientists, and business teams understand data within the context of the plant. We collaborate with the world’s largest companies and most innovative partners who are transforming the industrial sector. Cerebre is a global team of engineers, scientists, innovators, and explorers united by a mission to help the world’s largest manufacturing facilities build and use intelligence. Our development team is made up of world-class engineers who design and deliver novel solutions. This is an opportunity to join a market-leading team that is changing how the industrial world works. If you love building and creating value in the “white space,” if you thrive with the freedom and flexibility to think outside the box, if you are passionate about working with critical thinkers who challenge the status quo, and if you aspire to work in a fast-paced environment, then we would love to meet you! We believe flexibility drives creativity and that our team should live and work where we can be our best selves. We’re 100% remote and offer a competitive time-off package to ensure time for rest and recharge. 📌 About the role We are seeking a Senior Engineer to take ownership of critical components of our query and compilation systems. In this role, you will drive the design and evolution of the compiler pipeline, from parsing and AST construction through validation, optimization, and execution-plan generation, ensuring that complex queries are translated into efficient, scalable, and reliable operations across the platform. A core responsibility of this role is leading the design and implementation of advanced optimization strategies. You will develop and refine transformation pipelines, including rewrite rules and optimization passes, that significantly improve performance while maintaining strict correctness of query semantics. This includes optimizing how data is traversed, filtered, aggregated, and executed, with a strong awareness of backend behavior and system constraints. As a senior member of the team, you will operate with a high degree of ownership in ambiguous and complex problem spaces, shaping both system architecture and technical direction. You will collaborate closely with engineers across the platform to ensure that optimizations are robust, maintainable, and aligned with real-world usage patterns. Your work will directly influence system performance, scalability, and the overall developer and user experience. ⚙️ Key Responsibilities Maintain and extend the compiler pipeline, including grammar, AST construction, validation, compilation, and execution-plan generation. Design and implement optimization passes over the AST and execution plan, such as simplification, predicate pushdown, filter combination, dead-step elimination, repetition/path rewrites, and plan normalization. Improve query performance by identifying inefficiencies in traversal, filtering, projection, aggregation, and backend interaction. Preserve correctness of query semantics while introducing compiler optimizations and refactors. 📜 Required Skills Strong professional experience in C# and .NET. Experience maintaining compilers, interpreters, query planners, rule engines, or other transformation-heavy systems. Solid understanding of parsing, ASTs, semantic validation, and code or query generation. Experience designing optimization passes or rewrite rules with strong attention to semantic correctness. Ability to reason about execution plans, performance tradeoffs, and backend-aware optimization. Proficient in English language 🎯 Preferred Skills Experience with ANTLR or similar parser generators. Experience with query engines, graph databases, relational planners, or traversal-heavy workloads. Familiarity with optimization techniques such as constant folding, canonicalization, predicate pushdown, common-subexpression elimination, and plan simplification. Experience with gRPC and ASP.NET services in production systems. Experience profiling .NET applications and improving throughput or memory behavior in execution-heavy pipelines. More about cerebre 🚀 We are cross-functional collaborators. We blend manufacturing process knowledge with software and big data engineering expertise to create value in physical settings We are experienced. We are armed with industry-leading experts in numerical simulation, combustion, power, computational fluid dynamics, and chemical process modeling We are serious builders. We develop our platforms using leading practices in IT/OT architecture, OT security, AI architecture, ML Ops, and Platform engineering

🏢 Summary: Opportunity for a Senior AI Solutions Architect to design and lead production-grade LLM systems focused on retrieval-augmented generation and knowledge graph integration. The role involves building end-to-end orchestration layers, semantic search pipelines, and evaluation frameworks to deliver reliable, context-aware AI solutions. You will work with vector and graph databases, advanced embedding strategies, and enterprise AI platforms in a scalable cloud environment. 🗂️ Requirements: Experience designing and implementing LLM-based systems in production, Hands-on experience with retrieval-augmented generation (RAG), Strong knowledge of embeddings, vector search, and chunking strategies, Experience building entity extraction pipelines, Experience working with knowledge graphs, Proficiency in Python, Understanding of prompt engineering, Experience designing agent workflows, Experience defining evaluation frameworks and quality metrics for AI systems, Familiarity with distributed systems and scalable data architectures, Experience implementing observability, logging, and tracing 📃 Skills: Python, SQL, RAG, LLM, Embeddings, VectorDB, GraphDB, GCP, CloudSpanner, VertexAI, Gemini, IAM, Encryption, Observability 🏢 Description: Project overview This project focuses on building a scalable AI platform that transforms expert knowledge into structured graph based intelligence and connects it with enterprise grade language models. The solution emphasizes semantic search, agent workflows, and robust evaluation frameworks to ensure reliable outputs. Position overview We are looking for a Senior AI Solutions Architect to lead the design and implementation of advanced LLM driven solutions with a strong focus on retrieval augmented generation and knowledge graph integration. You will take ownership of the full orchestration layer, shaping how structured and unstructured data is transformed into high quality, context aware AI responses. Technology stack Python, SQL, vector databases, graph databases, Google Cloud Platform, Cloud Spanner, Vertex AI, Gemini, LLM frameworks, embedding models, observability tools, IAM, encryption Responsibilities Design and manage end to end LLM orchestration and retrieval pipelines Define embedding model selection and chunking strategies, including context window management and trade offs affecting retrieval quality and cost Own the entity extraction pipeline to convert unstructured content into graph nodes and relationships Implement entity resolution, relationship normalization, and deduplication processes Design and refine semantic search strategies and retrieval logic across graph and vector layers Develop prompt engineering approaches and agentic workflows for advanced use cases Integrate graph based outputs with enterprise AI platforms such as Gemini Design and maintain evaluation frameworks including ground truth dataset creation Measure and improve retrieval quality using metrics such as recall, precision at K, faithfulness, and answer relevance Establish systematic regression testing practices for AI pipelines Optimize LLM usage costs across the full retrieval and generation lifecycle Implement observability, logging, and tracing to monitor performance and reliability Requirements Experience designing and implementing LLM based systems in production environments Hands on experience with retrieval augmented generation and semantic search Strong understanding of embeddings, vector search, and chunking strategies Experience building entity extraction pipelines and working with knowledge graphs Proficiency in Python and data processing workflows Understanding of prompt engineering and agent workflow design Experience defining evaluation frameworks and quality metrics for AI systems Familiarity with distributed systems and scalable data architectures Experience implementing observability, logging, and tracing in data intensive environments Nice to have Experience with Google Cloud Platform services including Cloud Spanner and Vertex AI Familiarity with enterprise AI platforms such as Gemini Knowledge of cost optimization techniques for large scale LLM systems Experience with graph data models and hybrid architectures combining graph, relational, and vector data Exposure to advanced evaluation techniques for generative AI and ranking systems What We Offer: Vacation days : Up to 26 business days per year. 10 illness/special days off per year (fully paid, no medical papers needed) for all contract types Health and life insurance (Luxmed) MyBenefit platform with Multisport option Internal psychological support service English language classes from the first working day Access to external learning platforms : O’Reilly, LinkedIn Learning, Udemy, and a wide catalog of diverse internal training Flexible workplace : work from the office, from home, or choose a hybrid option Tech Skills Mentoring Program Opportunities to develop as a public speaker, mentor, or technical interviewer Fully paid idle (bench) when not involved in a project Certification reimbursement (AWS, GCP, Microsoft, etc.)

🏢 Summary: Forward Deployment Engineer role focused on embedding with clients to scope, build, and ship production-ready AI systems while contributing reusable assets to an internal AI platform. The position combines client delivery, pre-sales, and hands-on development, with ownership from discovery through production. It requires strong consulting capabilities and deep expertise in AI system design and deployment. 🗂️ Requirements: 5+ years of professional experience in software engineering, 3+ years hands-on experience building and shipping AI-assisted systems, 2+ years in client-facing consulting or pre-sales engineering role, End-to-end ownership: discovery, scoping, build, production, handoff, Experience running discovery workshops and gathering requirements, Ability to translate business needs into technical scope, Experience designing and deploying AI systems to production, Experience integrating with client systems and SaaS platforms, Experience with cloud deployment and security reviews, Fluency in Polish (B2+) and English (B2+) 📃 Skills: Python, TypeScript, LLMs, Claude, GPT, RAG, MCP, REST, iPaaS, CRM, BI, Cloud 🏢 Description: Description Our mission at Netguru is to help entrepreneurs and innovators shape the world through beautiful software. We care about trust, taking ownership, and transparency. As a Certified B Corporation®, we offer a safe, inclusive, and productive environment for all team members, and we’re always open to feedback. If you want to work from home and be a full-time employee, great! We want to create the right opportunities for you. Over the past ten years, Netguru has changed the way people bank, listen to music, learn languages, and rent bicycles. Some of their clients include Fortune 500 companies and startups like or . Netguru works with the largest brands in the world, such as , or . As the Forward Deployment Engineer you will sit at the intersection of client delivery, pre-sales, and internal AI tooling. You embed with a client (or an internal Netguru team), translate ambiguous problems into a defensible technical scope, and ship working AI systems to production - fast. Half of your time goes back into Netguru: every reusable pattern you spot in the field becomes a skill, agent, or MCP inside NetguruOS. This is a builder role, not an advisory one. Proof of work is a running system - not a deck. Salary: from 20000PLN net on B2B contract + 20 paid days off or from 16700PLN gross on Contract of Employment What you'll do: Client engagements: Embed temporarily into the team that owns the problem — client engineering org or internal Netguru team. Run discovery with stakeholders from CTO down to end-user. Run workshops, uncover requirements, clarify business needs, and translate ambiguous pain into a written technical scope you can defend. Analyze customer processes, identify bottlenecks, and propose digital or AI-driven improvements. Lead pre-sales on AI opportunities — scope, price, propose, close. Act as a trusted advisor, challenging assumptions and recommending better technical, product, or process approaches when appropriate. Communicate effectively with both technical and non-technical stakeholders, translating complex AI and engineering concepts into clear business language. Work comfortably within client environments, adapting to their tools, security requirements, workflows, governance models, and decision-making structures. Take ownership of outcomes, ensuring solutions deliver measurable business value, adoption, and long-term impact. Build Prototype in days, not weeks — working software, not slides. Write production-grade Python or TypeScript (or whatever the client runs). Ship AI-enabled workflows: copilots, agents, RAG systems, automation, internal tooling. Integrate with the client's existing stack. Design solutions that are not only technically sound but also usable, reliable, and aligned with real user workflows. Stand up evals, observability, and guardrails before going live. Own the path to production: security review, procurement, change management. Identify opportunities where AI can improve processes, reduce manual effort, accelerate decision-making, or create new product capabilities. Build internally (≈50% of the role) Extend Netguru's internal AI platform with new agents, MCPs, and skills based on field learnings Build enablement tooling for internal AI delivery teams — templates, runbooks, reusable skills Codify reusable client patterns back into NetguruOS Requirements Experience 5+ years total, including 3+ years hands-on building and shipping AI-assisted systems. 2+ years in a client-facing consulting or pre-sales engineering role. Proven full-cycle ownership: discovery → scoping → build → production → handoff. Comfortable with both technical and business stakeholders in the same engagement. Strong understanding of business processes and the ability to translate operational challenges into practical software and AI solutions. Experience gathering requirements, facilitating discovery workshops, and transforming ambiguous business problems into actionable implementation plans. Understanding of AI adoption challenges, including data readiness, user trust, governance, compliance, change management, and operational integration. Communicative (B2+) Polish and English. Technical Production-grade Python and/or TypeScript — shipped systems, not scripts. LLM stack fluency (Claude, GPT, open-weight) — knows when to use which and why. AI system design: agents, RAG pipelines, copilots, MCP servers, sub-agents. Eval-driven development — defines success metrics before shipping, measures after. Integration experience: REST APIs, iPaaS, common SaaS (CRM, PM, BI). Cloud deployment, security reviews, change management. Consulting & communication Strong customer-facing mindset with the ability to understand stakeholder needs and translate them into practical software solutions. Translates ambiguous client pain into written technical scope — and defends it. Credible with a CEO and an engineer on the same day. Proposal writing and deal scoping for AI engagements. Senior stakeholder management without hand-holding. Ability to communicate AI concepts, architecture decisions, risks, trade-offs, and implementation options to non-technical audiences. Solution-oriented consulting approach focused on delivering business outcomes rather than simply implementing requested features. Mindset Builder by default — running system over recommendation deck. Stack-agnostic — picks the right tool, not the favorite one. Walks into chaos, imposes structure, delivers clarity. Ships in days, not weeks. Thinks beyond delivery and takes responsibility for customer adoption, feedback, iteration, and measurable business impact. Nice to have: Domain experience in Finance, Retail, or Supply Chain Public presence - conference talks, open-source, published writing on AI SDLC orchestration and multi-agent pipelines What success looks like (first 6–12 months) First AI engagement scoped, built, and delivered to production At least one reusable asset (agent skill, MCP, eval suite) shipped into NetguruOS Measurable time savings from internal tooling contributions Benefits Access to the WorkSmile platform offering benefits adapted to your preferences: Multisport card. Private health insurance package. Life insurance. Hundreds of other options to choose from 15 categories (shopping, leisure, travel, food, etc.). Discounts on Apple products. Support for your growth - a head/manager’s budget available to every employee. PLN 175 monthly lump sum (ryczałt) for remote employees (Contract of Employment) Various internal initiatives: webinars, knowledge-sharing sessions, . Don't hesitate and apply right away! At Netguru, we're committed to creating an inclusive environment for everyone. If you require any disability-related accommodations during the recruitment process, please let us know. We're here to help!

🏢 Summary: Frontend engineering role focused on building enterprise-grade Next.js interfaces for AI-powered systems, including real-time AI interactions, semantic search, and knowledge-driven workflows. The position involves developing scalable, accessible, and high-performance UIs while contributing to frontend architecture, design systems, and AI-assisted engineering workflows. Candidates will work with modern AI tooling and enterprise application requirements in a fast-growing product environment. 🗂️ Requirements: 3–5 years frontend engineering experience, React expertise, TypeScript expertise, Production Next.js experience, SSR/SSG/CSR knowledge, State management experience, Experience with AI/LLM interfaces, Frontend performance optimization knowledge, Core Web Vitals understanding, Localization experience, Accessibility knowledge, Security best practices knowledge, AI coding assistants usage, Clear technical communication 📃 Skills: React, TypeScript, Next.js, SSR, SSG, CSR, JavaScript, LLM, AI, CoreWebVitals, Localization, Accessibility, Security, ReactFlow, Highcharts, RAG, MCP, DSPy, Node.js, Python, GitHubCopilot, Codex 🏢 Description: About Shelf The enterprise is going agentic and we are creating a unique operating system for an automated future. Most AI agents break the moment they hit real business complexity. That’s why we built a platform that models a company's policies, workflows, and operational logic into an AI Data Model. Shelf enables AI Agents to reliably reason and deliver precise and reliable outcomes at scale. We already have what most AI startups are still trying to earn: Enterprise customers such as Glovo, Nespresso, and HelloFresh, real production data, and tens of thousands of users. Customers trust us with operational knowledge: Our platform is highly reliable and secure with SOC2, HIPAA-ready security practices, and mature integrations in place. About the Role This role is for a frontend engineer with a few years of experience who wants to craft the interfaces that make enterprise AI accessible and trustworthy. While others build chatbots, we're solving the harder problem: how do you build UIs that help users confidently navigate AI-powered systems processing millions of documents, while maintaining enterprise-grade reliability? You'll build production Next.js applications that handle complex state, real-time AI streaming, and real enterprise functionality, growing into the harder architectural calls over time. We obsess over frontend quality because we're building the trust layer for AI itself: when your interfaces handle edge cases gracefully, give clear feedback during AI processing, and stay fast under load, you directly enable users to trust AI-powered answers and make critical business decisions. In our environment, AI isn't just what we build, it's how we build. You'll work alongside engineers who use AI coding assistants daily, build custom MCP servers, and experiment with new AI tooling to move faster. This is a role for someone in a high-growth chapter of their career, who wants to do the best work of their life, learn fast, and win as part of a team going all-in on a hard mission. What You Will Own Build and ship production Next.js applications with complex state and real-time AI interactions Build interfaces for AI-powered features: streaming LLM responses, semantic search, interactive knowledge graphs, and agentic builders Contribute to the component library and design system, keeping things reusable, accessible, and consistent Deliver enterprise-grade functionality: localization, accessibility, cross-browser support, and security best practices Help own what you ship after launch: monitor performance, optimize bundle sizes, and keep an eye on Core Web Vitals Use AI tools daily to move faster, and help improve internal frontend workflows Keep the bar high on code quality and testing through thoughtful code review What Strong Performance Looks Like You turn vague product problems into shipped, well-reasoned interfaces that users trust Complex AI behavior feels legible and controllable to users because of how you built the UI Your components are reusable, accessible, and perform well under real load You bring momentum. Work moves because you are on it What We Are Looking For Around 3 to 5 years of frontend engineering experience, with strong React and modern TypeScript Production Next.js experience: you've built and shipped SSR applications and understand the trade-offs between SSR, SSG, and CSR, with the appetite to go deeper Solid state management, with exposure to harder scenarios (real-time updates, optimistic UI, multi-tenant data) and the drive to grow there Experience building or contributing to AI/LLM interfaces: streaming responses, chat UIs, semantic search, or similar A feel for frontend performance: you care about Core Web Vitals and know the basics of improving them Exposure to enterprise application concerns: localization, accessibility, and security best practices A security-conscious approach to engineering: you handle sensitive data carefully and think about the security implications of what you ship Strong craft and product instinct: you spot UX issues and propose solutions, and you're not satisfied with "good enough" AI-native working style. You already use AI coding assistants daily and are eager to push the boundaries of AI-assisted development Clear communication. You articulate design decisions and technical trade-offs clearly, verbally and in writing Strong Plus Experience with React Flow, Highcharts, or other complex data-visualization libraries Hands-on experience with vector search UIs, RAG interfaces, or semantic search Familiarity with MCP, DSPy, or other cutting-edge AI frameworks, and building agentic AI interfaces Understanding of Node.js/Python backend fundamentals: you can read backend code and follow API design discussions Contributed to design systems or open-source component libraries A track record of growing fast and taking on more than your title strictly required How We Evaluate Fit We care more about craft, ownership, and the ability to make complex AI feel trustworthy than a perfect match to every library on paper. If you are the kind of engineer who can take a messy, complex enterprise problem and turn it into an interface people trust, we want to talk. What Shelf Offers B2B contract Company stock options Hardware: MacBook Pro Modern technical stack. Develop open-source software A strong AI-native engineering environment with modern tools and room to experiment, including Claude Code, OpenAI Codex, and GitHub Copilot Why Shelf Becoming one of the defining companies of the AI age is a hard plan, and our leadership team has the rare mix of deep AI, knowledge-management, and enterprise SaaS experience to actually execute it We have raised over $60 million in funding; our investors include Tiger Global, Insight Partners, Base10, and others Recognized by Gartner as a Cool Vendor, with high-velocity growth powered by the most innovative product in our category We love our customers and our customers love us. Ask a Shelf customer why, and they'll tell you it's our innovative capabilities and rock-solid reliability, that they enjoy working with our people, and most of all, the improvements they see in their business KPIs We're building fast across three hubs: our New York HQ and our Product and Engineering centers in Warsaw and Lviv

🏢 Summary: The role focuses on engineering production-grade AI features such as semantic search, intelligent assistants, and automated risk scoring by integrating external AI providers into a cybersecurity platform. You will own the full lifecycle from prototype to reliable, monitored production services, primarily using Python on Azure. This position emphasizes robust software engineering practices over model research or training. 🗂️ Requirements: 5+ years of professional experience building production systems in Python, Expert-level Python programming skills, Experience with ML frameworks and libraries, Proven experience shipping and operating production software, Experience integrating external APIs in production environments, Strong understanding of software architecture and design patterns, Practical experience with CI/CD pipelines, Experience with containerization using Docker, Understanding of AI/ML concepts including LLMs, embeddings, prompt engineering, RAG, Experience with unit and integration testing and mocking external services, Polish work permit, Professional proficiency in English 📃 Skills: Python, Azure, OpenAI, Anthropic, Docker, CI/CD, FastAPI, Flask, Pinecone, Weaviate, Chroma, .NET, REST, LLMs, RAG, Embeddings, Git 🏢 Description: About the Role As a Senior AI/ML Engineer you will be building AI-powered features at the core of our platform - semantic search, an intelligent assistant, automated risk scoring, and much more. We mainly integrate with external AI model providers (OpenAI, Anthropic, and others) to deliver these capabilities to our clients and internal users. This is a software engineering role, not a data science or ML research position. You won't train models from scratch or publish papers. You'll engineer production-grade systems that leverage AI to solve real cybersecurity problems. If you're a strong Python engineer excited about making AI reliable, maintainable, and impactful — we want to talk to you. We're looking for a versatile software engineer to own the journey from AI prototype to reliable production system: building robust integrations with external AI providers or fine-tuned models, standardizing our Python codebase and release pipelines, and ensuring our AI features meet the same quality bar as any other production service. What You'll Do Ship AI features end to end - from integration design through deployment to monitoring in production. You'll build and maintain Python services that power our AI-driven capabilities on Azure. Build production-ready AI / ML integrations - including retry logic, rate limiting, cost monitoring, fallback strategies, and abstraction layers that allow us to swap between model providers. Turn proofs of concept into reliable software - collaborate with our AI experts (who bring deep ML/data science expertise) to take working prototypes and engineer them into tested, documented, production-grade services. Standardize Python engineering practices - establish and evolve our code organization, testing strategies, CI/CD pipelines, and release processes for AI/ML features. Design clean service boundaries - our platform's core runs on .NET. You'll design and maintain well-defined APIs between the Python/AI layer and the rest of the system. Mentor and share knowledge - help other engineers grow their Python and AI engineering skills, and bring engineering rigor to the broader team's practices. Contribute to architecture decisions - work closely with other teams to shape how AI capabilities evolve within the platform. Who You Are Must-Haves 🔹 5+ years of professional experience building and maintaining production systems in Python. 🔹 Expert-level programming skills and proficiency with common ML frameworks and libraries. 🔹 A track record of shipping and operating software - not just building prototypes. You care about testing, monitoring, logging, and what happens after deployment. 🔹 Experience integrating external APIs in production systems - ideally AI/LLM provider APIs (OpenAI, Anthropic, Azure AI, or similar), but strong API integration experience in any domain counts. 🔹 Solid understanding of software architecture and design patterns - you have opinions about how to structure a Python project and can articulate trade-offs. 🔹 Practical knowledge of CI/CD pipelines, containerization (Docker), and modern development workflows. 🔹 A working understanding of AI/ML concepts - you know what embeddings, LLMs, prompt engineering, and RAG are. You can have a productive technical conversation with an ML specialist without needing every concept explained from scratch. 🔹 Experience with testing strategies - unit tests, integration tests, mocking external services. You instinctively write tests, not as an afterthought. 🔹 A highly adaptable, problem-solving and result-oriented mindset with the ability to work independently and take ownership in a fast-paced and dynamic environment. 🔹 Strong communication skills in English (our working language). 🔹 Willingness to learn continuously and share what you learn with the team. Nice-to-Haves 🔸 Experience with Azure cloud services. 🔸 Familiarity with FastAPI, Flask, or similar Python web frameworks. 🔸 Exposure to MLOps tooling or practices (model versioning, experiment tracking, feature stores). 🔸 Hands-on experience in Document AI / Intelligent Document Processing using traditional models and Generative AI. 🔸 Experience with vector databases (Pinecone, Weaviate, Chroma, or similar). 🔸 Any experience with .NET - helpful for cross-team collaboration, but not expected. 🔸 Background in or interest in cybersecurity as a domain. Your Team You'll join our Synapse team - a small, focused group responsible for all AI/ML capabilities within our platform. The team consists of: -> Team Leader / Architect - provides high-level architectural guidance, planning, and cross-stack delivery. -> AI / ML Engineer - deep expertise in AI/ML and data science. Your counterbalance: they bring the ML knowledge, you bring the production engineering discipline. -> AI / Backend Engineer - transitioning from .NET into AI engineering. A teammate you'll mentor and grow alongside. -> You - the person who bridges the gap between AI prototypes and reliable production systems. Why Join Us? / What We Offer: Competitive salary and benefits package. Remote work options and flexible working hours. Actual impact on the choice and shape of solutions developed. Opportunities for professional growth and development. Training and conference budget. A collaborative, innovative work environment with an iterative agile approach. The role is for six months with the possibility of extension. Both full-time and part-time cooperation are possible.

🏢 Summary: Full-time remote Senior AI Engineer role focused on designing and operating production-grade GenAI solutions, including agentic workflows and RAG pipelines integrated into enterprise systems. The position emphasizes software engineering excellence, cloud-based AI services, and orchestration of LLM capabilities rather than model research. You will build reliable, secure, and observable AI services integrated with internal platforms and APIs. 🗂️ Requirements: 5+ years of professional software development experience, Strong background in designing and operating production systems, Proficiency in Python or another backend language for AI systems, Experience building and integrating RESTful APIs, Understanding of distributed systems fundamentals, Experience with API integrations and event-driven architectures, Solid SQL and database design knowledge, Experience with vector search, Hands-on experience building end-to-end LLM applications, Experience implementing RAG pipelines, Strong knowledge of Azure cloud services, Experience with unit and integration testing, Ability to build AI evaluation and regression testing frameworks 📃 Skills: Python, REST, GraphQL, SQL, Azure, RAG, LLM, LangChain, LangGraph, ASP.NET, APIs, Embeddings, VectorSearch, AzureFunctions, AzureStorage, KeyVault, AppConfiguration, ApplicationInsights 🏢 Description: Workload: full-time Work model: 100% Remote We are seeking a Senior AI Engineer who combines strong software engineering fundamentals with hands-on experience building production GenAI solutions, including agentic workflows and Retrieval-Augmented Generation (RAG). This is an engineering and orchestration role focused on integrating LLM capabilities into enterprise systems – not a traditional model-training/ML research role. 5+ years of professional software development experience (ideally 7+ years across backend/API/integration and cloud platforms). Proven ability to ship production-grade LLM applications ( RAG , tool/function calling, agent orchestration) with reliability, security, and observability. Strong ownership mindset and passion for AI engineering – curiosity, experimentation, and a drive to continuously improve the product and the team. Excellent communication and collaboration skills; ability to guide, mentor, and unblock other engineers as we build out an AI engineering capability. Main Responsibilities: Design, build, and operate agentic AI services that orchestrate tools, workflows, and integrations across cloud systems and enterprise data sources. Implement and continuously improve RAG pipelines for tax artifacts and internal knowledge, including ingestion, retrieval tuning, and evaluation. Integrate AI workflows with existing internal platforms (e.g., assistant frameworks) and back-end services through robust APIs. Define and maintain tool/function schemas and orchestration patterns; implement streaming updates, interrupts, and human-in-the-loop steps as needed. Partner with other engineers to set direction, mentor, and unblock the team — helping establish strong foundations for the AI initiative. Build in quality from day one: automated tests, evaluation checks, monitoring/telemetry, and performance optimization for network-bound workloads. Participate in Agile ceremonies (daily scrums, refinement/grooming, planning) and collaborate through peer review, pair programming, and strong documentation. Apply best practices, design principles, and security standards throughout the SDLC, with a focus on reliability and responsible AI. Key Requirements: Strong software engineering background (not a research-only data science profile): designing, building, and operating production systems. Proficiency in at least one backend language used for AI systems (Python preferred). Hands-on experience building and integrating RESTful APIs ; GraphQL experience is a plus. Strong understanding of distributed systems fundamentals : concurrency, async I/O, resiliency/retries, rate limits, caching, and performance optimization. Experience integrating with external services and internal platforms via APIs and event-driven patterns. Solid database fundamentals (SQL design, performance, migrations); experience with vector search is required, and hybrid search stores are a plus. Hands-on experience building LLM-powered applications end-to-end : prompt design, tool/function interfaces, structured outputs, and streaming user experiences. Experience with RAG systems : document ingestion pipelines, chunking/metadata, embeddings, retrieval strategies, grounding, and evaluation. Cloud services expertise : Strong knowledge of Azure cloud services used for enterprise AI solutions (e.g., Functions, Storage, Key Vault, App Configuration, Application Insights). Development practices experience : Strong background in unit and integration testing; ability to build and maintain AI evaluation harnesses (golden sets, regression tests, automated checks). Nice to Have: Direct experience with LangGraph and/or LangChain for multi-agent workflows. Familiarity with emerging agentic ecosystem concepts/protocols (e.g., MCP, A2A, ADK or similar). Experience integrating AI services into .NET (ASP.NET Core) applications or building AI microservices that serve enterprise applications. Experience with event-driven architectures (service bus, event hubs) and real-time updates/streaming to UI. Experience working with tax/enterprise document corpora and governance constraints (PII, retention, access control). Other Details: This position is designed for remote work and has a flexible duration, allowing for innovation in the AI space within an agile environment.

🏢 Summary: Full-time remote Senior AI Engineer role focused on designing and operating production-grade GenAI systems, including agentic workflows and RAG pipelines integrated into enterprise platforms. The position emphasizes backend engineering, API integrations, and orchestration of LLM capabilities in secure, scalable cloud environments rather than ML research. The role also includes mentoring engineers and establishing best practices for reliable, observable AI services. 🗂️ Requirements: 5+ years professional software development experience, Proficiency in Python, Experience building production LLM applications, Hands-on experience with RAG systems, Experience designing and integrating RESTful APIs, Strong understanding of distributed systems fundamentals, Experience with SQL databases and performance optimization, Experience with vector search, Experience integrating external services via APIs, Knowledge of Azure cloud services, Experience with unit and integration testing, Ability to build AI evaluation and testing frameworks 📃 Skills: Python, REST, GraphQL, SQL, Azure, RAG, LLM, LangChain, LangGraph, ASP.NET, APIs, Microservices, DistributedSystems, Concurrency, AsyncIO, VectorSearch, Embeddings, Caching, AzureFunctions, Storage, KeyVault, AppConfiguration, ApplicationInsights, ServiceBus, EventHubs 🏢 Description: Workload: full-time Work model: 100% Remote We are seeking a Senior AI Engineer who combines strong software engineering fundamentals with hands-on experience building production GenAI solutions, including agentic workflows and Retrieval-Augmented Generation (RAG). This is an engineering and orchestration role focused on integrating LLM capabilities into enterprise systems – not a traditional model-training/ML research role. 5+ years of professional software development experience (ideally 7+ years across backend/API/integration and cloud platforms). Proven ability to ship production-grade LLM applications ( RAG , tool/function calling, agent orchestration) with reliability, security, and observability. Strong ownership mindset and passion for AI engineering – curiosity, experimentation, and a drive to continuously improve the product and the team. Excellent communication and collaboration skills; ability to guide, mentor, and unblock other engineers as we build out an AI engineering capability. Main Responsibilities: Design, build, and operate agentic AI services that orchestrate tools, workflows, and integrations across cloud systems and enterprise data sources. Implement and continuously improve RAG pipelines for tax artifacts and internal knowledge, including ingestion, retrieval tuning, and evaluation. Integrate AI workflows with existing internal platforms (e.g., assistant frameworks) and back-end services through robust APIs. Define and maintain tool/function schemas and orchestration patterns; implement streaming updates, interrupts, and human-in-the-loop steps as needed. Partner with other engineers to set direction, mentor, and unblock the team — helping establish strong foundations for the AI initiative. Build in quality from day one: automated tests, evaluation checks, monitoring/telemetry, and performance optimization for network-bound workloads. Participate in Agile ceremonies (daily scrums, refinement/grooming, planning) and collaborate through peer review, pair programming, and strong documentation. Apply best practices, design principles, and security standards throughout the SDLC, with a focus on reliability and responsible AI. Key Requirements: Strong software engineering background (not a research-only data science profile): designing, building, and operating production systems. Proficiency in at least one backend language used for AI systems (Python preferred). Hands-on experience building and integrating RESTful APIs ; GraphQL experience is a plus. Strong understanding of distributed systems fundamentals : concurrency, async I/O, resiliency/retries, rate limits, caching, and performance optimization. Experience integrating with external services and internal platforms via APIs and event-driven patterns. Solid database fundamentals (SQL design, performance, migrations); experience with vector search is required, and hybrid search stores are a plus. Hands-on experience building LLM-powered applications end-to-end : prompt design, tool/function interfaces, structured outputs, and streaming user experiences. Experience with RAG systems : document ingestion pipelines, chunking/metadata, embeddings, retrieval strategies, grounding, and evaluation. Cloud services expertise : Strong knowledge of Azure cloud services used for enterprise AI solutions (e.g., Functions, Storage, Key Vault, App Configuration, Application Insights). Development practices experience : Strong background in unit and integration testing; ability to build and maintain AI evaluation harnesses (golden sets, regression tests, automated checks). Nice to Have: Direct experience with LangGraph and/or LangChain for multi-agent workflows. Familiarity with emerging agentic ecosystem concepts/protocols (e.g., MCP, A2A, ADK or similar). Experience integrating AI services into .NET (ASP.NET Core) applications or building AI microservices that serve enterprise applications. Experience with event-driven architectures (service bus, event hubs) and real-time updates/streaming to UI. Experience working with tax/enterprise document corpora and governance constraints (PII, retention, access control). Other Details: This position is designed for remote work and has a flexible duration, allowing for innovation in the AI space within an agile environment.

🏢 Summary: Opportunity for a Forward Deployed Engineer (GTM) to lead the technical side of enterprise AI platform deals, owning discovery, architecture design, and proof-of-concept delivery for production-grade agentic AI systems. The role combines backend engineering, AI/ML expertise, and customer-facing technical leadership to drive successful adoption of an AI control plane and Kubernetes-based compute platform. You will shape production AI architectures, build integrations and workflows, and influence product direction based on field feedback. 🗂️ Requirements: 3–7 years of backend software engineering experience, Production systems ownership and delivery experience, Proficiency in Python, Go, or TypeScript, Experience with Kubernetes or major cloud platforms (AWS, Azure, or GCP), Hands-on experience with LLMs, agents, evals, or ML pipelines in production, Ability to lead architecture discussions with engineering stakeholders 📃 Skills: Python, Go, TypeScript, Kubernetes, AWS, Azure, GCP, LLM, LangChain, vLLM, SGLang, Triton, MCP, ML, MLOps 🏢 Description: About TrueFoundry Every production AI system whether it's powering customer support, writing code, analyzing financial data, or diagnosing medical conditions needs the same foundational infrastructure. A way to route between models. A way to manage tools and integrate them securely. A way to orchestrate agents and enforce governance. A unified compute layer to run it all. That infrastructure layer is being built right now. We're TrueFoundry, and we're building it. We're looking for a Forward Deployed Engineer- GTM to join the team. The Problem We're Solving Companies are moving beyond simple chatbots to production agentic systems. These systems route between OpenAI, Anthropic, Google, and self-hosted models. They integrate dozens of tools via protocols like MCP. They orchestrate multi-agent workflows where agents coordinate with other agents. The infrastructure to support this doesn't exist yet. You can't just duct-tape together a few API calls and call it production-ready. You need a control plane that handles: - Intelligent routing with observability, cost policies, and fallback logic - Centralized tool and MCP server management with security and lifecycle controls - Agent orchestration with governance and guardrails - A unified compute layer to run self-hosted models, custom tools, and agents We've built two products to solve this: AI Gateway is the control plane five composable components (Prompts, LLM Gateway, MCP Gateway, Guardrails, Agent Gateway) that handle routing, orchestration, and governance. AI Deploy is the compute layer of Kubernetes-based platform that abstracts ML workloads as standard software primitives, so everything runs on unified infrastructure. We're Series A, backed by Intel Capital and Sequoia. Companies like CVS, Mastercard, Siemens, Paytm, Synopsys, and Zscaler run production AI workloads on our platform. What you'll do - Own the technical arc of the deal: discovery and the in-depth demo, the architecture discussion with the customer's engineering leaders, and the POC - all the way through to close. - Win the POC on technical merit: Co-define the success criteria with the customer, run the execution tracker and the milestones, and write the integration glue, agent workflows, and eval pipelines that prove value in their environment. The goal is delivered impact, not lines of code. - Make the architecture calls that decide how the customer would run AI in production. You are the technical decision-maker in the room - the CTO in the room - and the person their engineering leaders trust. - Be the technical face of TrueFoundry to enterprise customers, and earn the credibility that turns an evaluation into a signed contract. - Steer the product. You see, first-hand, what customers pull toward and where their first production win lands. Compile that field signal and bring structured, data-backed asks to TrueFoundry's engineering and product teams - your intuition helps shape the roadmap. Who we're looking for - Backend Engineering Experience: 3 to 7 years of backend software engineering experience with production systems you have shipped and owned. Strong in Python, Go, or TypeScript. Comfortable with Kubernetes or any major cloud (AWS, Azure, GCP). - Business Acumen: You don't just question the how but also the why - discovering impact, architecture design and deal-winning opportunities. - AI and ML literacy: You have worked with LLMs, agents, evals, or ML pipelines in production. Familiarity with the modern stack (vLLM, SGLang, LangChain, Triton, model serving) is a strong plus. - Design and Architecture: Comfortable leading an architecture discussion with a customer's engineering leaders, then doing the work yourself. High agency and a bias for action. We especially welcome founding engineers and CTOs of early-stage startups, as well as engineers from infrastructure, MLOps, and developer-tool companies. Growth path Engineering ladder: Senior FDSE to Staff or Principal FDSE, with named-account ownership and architectural authority at every level. Leadership ladder: Lead FDSE to Head of FDSE, building and running the function. Cross-functional: the role builds the breadth that leads to CTO, VP of Engineering, and founder paths. Many FDSE alumni have gone on to start their own companies.