Senior Python Developer

🏢 Summary: Senior-level role focused on designing and delivering scalable AI/ML solutions, including LLMs, RAG, and agent-based systems, from architecture to production deployment. The position involves leading technical decisions, writing high-quality Python code, and building cloud-based microservices solutions. The role also requires translating business needs into robust, production-ready AI systems. 🗂️ Requirements: Several years of software development experience (senior-level), Strong Python proficiency, Solid understanding of ML/AI ecosystem (LLMs, NLP, deep learning), Hands-on experience with RAG, prompt engineering, agent-based systems, Experience with microservices architecture, Experience with databases, Experience with Kubernetes, Experience with CI/CD pipelines, Experience with Azure cloud, English proficiency 📃 Skills: Python, LLMs, RAG, NLP, DeepLearning, PromptEngineering, Microservices, Databases, Kubernetes, CICD, Azure, GCP, Java 🏢 Description: Responsibilities: Design and develop scalable AI/ML solutions (LLMs, RAG, agentic systems) Write high-quality, testable Python code Lead technical architecture and make key design decisions for critical projects Own end-to-end delivery of solutions, from design to production deployment Collaborate with business stakeholders and translate requirements into technical solutions Requirements: Several years of experience as a software developer (senior-level preferred) Very strong Python skills and solid understanding of the ML/AI ecosystem (LLMs, NLP, deep learning) Hands-on experience with RAG, prompt engineering, and agent-based systems Experience with microservices architecture, databases, Kubernetes, and CI/CD pipelines Experience with Azure cloud (GCP as a plus) Basic familiarity with Java is a plus Strong English communication skills

🏢 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: 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: Senior AI Engineer role focused on building and deploying production-grade AI solutions using Python, FastAPI, Azure, Generative AI, RAG, and agentic frameworks in a hybrid work model. The position includes backend architecture, AI pipeline development, MLOps ownership, and mentoring responsibilities. 🗂️ Requirements: 6–10 years of software engineering experience, Strong AI/ML engineering background, Expertise in Python, Expertise in FastAPI, Experience with LLMs, Experience with RAG pipelines, Experience with vector databases, Hands-on experience with agentic AI frameworks, Production experience with Azure cloud services, Knowledge of MLOps, Experience with Docker, Experience with Kubernetes, Experience with CI/CD 📃 Skills: Python, FastAPI, Azure, LLMs, RAG, Pinecone, Milvus, LangChain, LangGraph, AutoGen, CrewAI, Docker, Kubernetes, CI/CD, MLOps, AKS 🏢 Description: Location: Wroclaw, Poland (Hybrid: 3 days/week in-office) Experience: 6–10 years About the Role We are looking for a Senior AI Engineer to design, develop, and deploy production-grade AI solutions using Python, FastAPI, and Azure, with a strong focus on Generative AI, LLMs, RAG, and agentic frameworks. Key Responsibilities Architect and implement scalable backend services with Python and FastAPI . Build and optimize RAG pipelines using vector databases (e.g., Pinecone, Milvus, Azure Cognitive Search). Develop autonomous agents using agentic AI frameworks (LangChain, LangGraph, AutoGen). Deploy and manage AI workloads on Azure (Azure ML, AKS, OpenAI Service). Own the full ML lifecycle: experimentation, versioning, CI/CD, and monitoring. Mentor junior engineers and drive technical best practices. Required Skills 6–10 years of software engineering experience, with strong focus on AI/ML. Expert in Python and FastAPI . Deep experience with LLMs , RAG , and vector databases . Hands-on experience with agentic frameworks (LangChain, CrewAI, etc.). Production experience with Azure cloud services. Strong understanding of MLOps, containerization (Docker/Kubernetes), and CI/CD.

🏢 Summary: The offer is for a backend-focused AI Engineer role building a modern AI platform for business process automation and multi-agent system integration in a production-grade enterprise environment. The position centers on developing scalable Python-based services, secure API integrations, and cloud-native AI solutions using LLMs and RAG. The role involves designing robust, secure, and scalable architectures deployed on AWS or Azure with containerized infrastructure. 🗂️ Requirements: Extensive experience in backend development with Python in production environments, Hands-on experience with AI Agents, RAG, MCP, or LLM-based solutions, Experience with system integrations and API architecture, Experience with scalable cloud-based applications, Experience with AWS or Azure, Experience with Docker and Kubernetes, Experience designing secure and scalable enterprise solutions, Experience with multi-agent systems and workflow automation, Ability to conduct code reviews and mentor developers, Fluent English and Polish 📃 Skills: Python, LLM, RAG, MCP, AWS, Azure, Docker, Kubernetes, API, Microservices, Cloud, AI, Agents 🏢 Description: Additional information The project focuses on building and developing a modern AI platform supporting business process automation and the integration of intelligent agents with enterprise systems. The team is creating scalable solutions based on Python, cloud technologies, and multi-agent architecture, leveraging technologies such as LLMs, RAG, and advanced workflow automation. A key aspect of the project is designing secure integrations between AI platforms, internal systems, and external APIs within a production-grade environment. The project is delivered in an international technology-driven environment with a strong focus on innovation, high-quality architecture, and the development of cutting-edge AI solutions for a global industrial organization. You’re ideal for this role if you: Have extensive experience in backend development with Python in production environments Have hands-on experience with AI technologies such as AI Agents, RAG, MCP, or LLM-based solutions Understand system integrations, API architecture, and scalable cloud-based applications Have worked with AWS or Azure and containerized environments such as Docker and Kubernetes Are experienced in designing secure and scalable enterprise solutions Have strong problem-solving skills and can proactively remove technical blockers Enjoy working closely with cross-functional teams in an international environment Have experience mentoring developers, conducting code reviews, and promoting engineering best practices Are interested in modern AI architectures, workflow automation, and multi-agent systems Communicate fluently in English and Polish and feel comfortable working in a global setup Your day-to-day responsibilities include:​ Designing and developing scalable backend services and AI-driven integration solutions in Python Building and maintaining APIs, connectors, and middleware between AI platforms and enterprise systems Creating and optimizing multi-agent workflows and intelligent automation processes Collaborating with AI Engineers, Data Engineers, and other technical stakeholders on system architecture and integrations Integrating external AI platforms, internal tools, and cloud services into production environments Working with cloud technologies and containerized infrastructure in AWS or Azure environments Improving the quality, reliability, and performance of AI-powered solutions and LLM-based workflows Conducting code reviews, mentoring team members, and supporting engineering best practices Troubleshooting technical issues, identifying bottlenecks, and driving continuous improvements Contributing to technical documentation, architecture discussions, and innovation initiatives across the team

🏢 Summary: Hybrid Python AI Developer role focused on building AI-powered services and automation solutions using Large Language Models and agent-based workflows. The position involves developing RAG-based bots and assistants, implementing agentic workflows, and collaborating on high-quality AI frameworks. It offers the opportunity to work on advanced LLM solutions in a dynamic, innovation-driven environment. 🗂️ Requirements: Minimum 3 years of experience in software development with Python, Practical knowledge of LangChain and LangGraph, Experience with RAG, embeddings, vector search, LLM agents, agentic workflows, tool calling, At least 1 year of hands-on experience with LLM models, RAG, and AI agents in real projects, Proficiency with Git (branch management, rebase, cherry-pick, revert, squash), Familiarity with Jira, Confluence, Bitbucket, Competent command of Polish, Communicative level of English, Legal right to work in the European Union 📃 Skills: Python, LangChain, LangGraph, RAG, LLM, Embeddings, VectorSearch, Git, Jira, Confluence, Bitbucket, Azure, React, Streamlit, Databricks, CosmosDB, Redis, Grafana 🏢 Description: Unleash the future of AI by building innovative Large Language Model solutions that redefine automation and intelligence. Warszawa-based opportunity with hybrid work model (4 days remote, 1 day in the office). As a Python AI Developer , you will be working for our client, a leading innovator in AI technology. You will contribute to developing cutting-edge services and applications supporting automation processes, leveraging advanced LLM solutions and agent-based workflows. This role offers a chance to be at the forefront of AI innovation and accelerate your career growth in a dynamic environment. Your main responsibilities: Build AI-powered services and applications in Python to support automation initiatives. Develop bots and assistants based on Large Language Models, including RAG solutions and multi-step task agents. Implement agentic workflows: define roles, manage tools, and oversee execution processes. Work with internal and new AI frameworks, ensuring high quality and alignment with best practices. Collaborate with cross-functional teams to troubleshoot, analyze issues, and optimize solutions. Maintain adherence to development standards and contribute to continuous improvement initiatives. Document system designs and participate in architectural discussions. You're ideal for this role if you have: Minimum 3 years of experience in software development, particularly with Python. Practical knowledge of LangChain and LangGraph. Experience with RAG, embeddings, vector search, LLM agents, agentic workflows, and tool calling. At least 1 year of hands-on experience with LLM models, RAG, and AI agents in real projects. Proficiency with Git, including branch management and commit operations (rebase, cherry-pick, revert, squash). Familiarity with Jira, Confluence, Bitbucket. Strong problem-solving skills and ability to work independently in a collaborative environment. It is a strong plus if you have: Knowledge of Azure AI Stack (AI Foundry, Azure OpenAI, Azure AI Search). Experience with React, Streamlit, Databricks, Cosmos DB, Redis, or Grafana. Understanding of ML fundamentals and fine-tuning LLMs. Skills in high-level system architecture design and documentation. Language Required for the role: Polish – competent command. English – communicative level. Eligibility for the role: Only candidates with an existing legal right to work in the European Union will be considered. #MAKEYourCareerBETTER Interested? Apply now and include your CV (preferably in English) along with a statement confirming your consent to the processing and storage of your personal data. Please be prepared for a live coding session during the interview process, where your technical skills will be thoroughly evaluated.

🏢 Summary: The role involves architecting and deploying advanced AI-driven applications using Azure AI services and modern generative AI frameworks. The position focuses on building scalable knowledge retrieval systems, intelligent AI agents, and integrating AI capabilities into existing products. It requires strong expertise in cloud-native, containerized, and MLOps-driven environments. 🗂️ Requirements: Practical experience with Azure AI ecosystem, Experience with GitHub and DevOps methodologies, Strong programming skills in Python, Hands-on experience with embeddings and text chunking, Experience with vector search solutions, Experience with FastAPI and Pydantic, Experience with pymongo and Azure SDKs, Experience designing scalable AI systems, Experience building document processing pipelines, Experience with cloud-native infrastructures 📃 Skills: Azure, AzureOpenAI, AzureDocumentIntelligence, OCR, LangChain, RAG, CAG, MongoDB, PostgreSQL, Python, FastAPI, Pydantic, pymongo, Docker, GitHub, DevOps, MLOps, Embeddings, VectorSearch, JSON 🏢 Description: We are looking for a skilled AI Full-Stack Developer to architect, implement, and deploy advanced AI-driven applications. The ideal candidate will work with Azure AI services, MongoDB Atlas, LangChain-based workflows, RAG/CAG frameworks, and containerized environments (Docker), while leveraging modern AI agent technologies. Key Responsibilities Design and implement Generative AI solutions using Azure OpenAI, Azure Document Intelligence, OCR capabilities, and other Azure cloud services. Work closely with front-end and back-end teams to seamlessly embed AI functionalities into existing systems. Review and maintain high-quality code for AI service integrations, ensuring best practices and performance standards are met. Create scalable knowledge retrieval systems using LangChain, RAG/CAG approaches, and vector databases—primarily MongoDB Atlas—with working knowledge of PostgreSQL. Develop intelligent AI agents capable of autonomous reasoning, planning, and interaction with external tools. Support and promote AI-augmented development practices with tools such as GitHub Copilot or Factory AI. Requirements Practical experience with the Azure AI ecosystem, GitHub, DevOps methodologies, generative AI architectures, and vector search solutions. Strong programming skills in Python and familiarity with modern MLOps and DevOps environments. Hands-on experience with embeddings and text chunking strategies, JSON and markup formats, FastAPI, Pydantic, pymongo, and Azure SDKs. Proven expertise in designing scalable AI systems, document processing pipelines, and cloud-native infrastructures. Strong communication abilities and experience collaborating within cross-functional teams.

🏢 Summary: Opportunity to join a newly formed AI team in the banking sector to build a modern AI-native platform from scratch. The role focuses on designing and delivering production-grade solutions using LLMs, RAG, and agent-based architectures, while shaping AI strategy and architectural decisions. You will develop secure, transparent, and scalable AI systems that drive real business value in a regulated financial environment. 🗂️ Requirements: At least 3 years of experience in AI/ML, Minimum 5 years of experience in product development, Strong Python skills, Hands-on experience with LLMs, RAG, and agent-based systems in production, Experience with AI frameworks such as Agno, LangChain, LlamaIndex or similar, Knowledge of prompt engineering and AI model evaluation, Experience with Azure or AWS, Understanding of vector databases and embeddings, Experience with workflow orchestration tools (Temporal, Prefect, Airflow), Ability to design scalable and reliable AI solutions, Experience in model monitoring and MLOps practices, Ability to make architectural decisions and assess business value of AI, Fluent English 📃 Skills: Python, LLM, RAG, LangChain, LlamaIndex, Agno, Azure, AWS, Temporal, Prefect, Airflow, MLOps, Embeddings, VectorDB 🏢 Description: We are looking for an experienced Senior AI Engineer to join a newly formed AI team delivering a strategic initiative in the banking sector. This is a unique opportunity to co-create a modern AI-native platform from scratch and influence the direction of AI within the organisation. In this role, you will design and develop solutions based on LLMs, RAG systems, and agent-based architectures to support data integration and transformation processes. You will contribute to key decisions around AI strategy, model selection, architecture, and quality standards. We are seeking a candidate with strong technical skills and hands-on experience in building production-grade AI solutions—someone who understands where AI brings real business value and can deliver high-quality, transparent, and secure solutions in a financial environment. Your tasks Design and develop solutions based on LLMs, RAG, and agent-based architectures Co-create and execute an AI strategy for a modern banking platform Select models, frameworks, and approaches for production-grade AI solutions Build systems for data integration, transformation, and validation automation Define prompt engineering, model evaluation, and AI quality standards Develop monitoring, evaluation, and continuous improvement mechanisms Collaborate with engineering and product teams on new features Ensure high quality, transparency, and auditability of AI outputs Contribute to key architectural decisions and AI direction Mentor and share knowledge within the AI team Requirements At least 3 years in AI/ML and a minimum of 5 years in product development Strong Python skills Hands-on experience with LLMs, RAG, and agent-based systems in production Familiarity with frameworks such as Agno, LangChain, LlamaIndex, or similar Knowledge of prompt engineering and AI model evaluation Experience with Azure or AWS Understanding of vector databases and embeddings Experience with workflow orchestration tools (e.g., Temporal, Prefect, Airflow) Ability to design scalable, reliable AI solutions Experience in model monitoring and MLOps practices Capability to make architectural decisions and assess the business value of AI Strong collaboration skills with technical and business stakeholders Fluent English Nice-to-have requirements Previous work in financial services, fintech, or other regulated industries Knowledge of compliance, auditability, and AI security Familiarity with MLOps and large-scale ML/LLM deployments Experience fine-tuning open-source models for specific domains Proven track record in developing AI solutions for data platforms, analytics, or SaaS products Background in AI R&D projects Publications, conference speaking, or open-source contributions Experience building and scaling technical teams What we offer Great Place to Work since 2015 - it’s thanks to feedback from our workers that we get this special title and constantly implement new ideas Employment stability - revenue of PLN 2.1BN, no debts, since 2006 on the market We share the profit with Workers - over PLN 76M has already been allocated for this aim since 2022 Attractive benefits package - private healthcare, benefits cafeteria platform, car discounts and more Comfortable workplace – class A offices or remote work Dozens of fascinating projects for prestigious brands from all over the world PLN 1 000 000 per year for your ideas - with this amount, we support the passions and voluntary actions of our workers Investment in your growth – meetups, webinars, training platform and technology blog – you choose Fantastic atmosphere created by all Sii Power People If you want to work on systems with high operational significance — apply now!

🏢 Summary: Python AI Engineering role focused on building production-grade GenAI, RAG, and agentic systems for client engagements. The position involves end-to-end ownership of AI solutions, including architecture, delivery, evaluation, and client communication in a fast-paced consulting environment. Candidates will work with scalable APIs, LLM pipelines, and cloud AI services while ensuring quality, observability, and performance. 🗂️ Requirements: 5+ years commercial Python experience, 3+ years hands-on GenAI/LLM engineering, Experience building scalable services and APIs, Experience with FastAPI, Flask, or Django, Experience writing test suites with pytest, Experience building RAG pipelines, Knowledge of embeddings and vector search, Experience with agentic workflows and tool-calling, Strong prompt engineering skills, Experience with structured outputs and JSON schemas, Experience with Pydantic or equivalent, Experience with AWS, Azure, or GCP, Understanding of managed AI/ML services, Ability to evaluate relevance, latency, consistency, and cost, High-proficiency written and spoken English 📃 Skills: Python, GenAI, LLM, FastAPI, Flask, Django, pytest, RAG, Embeddings, VectorSearch, ToolCalling, PromptEngineering, JSON, Pydantic, Instructor, AWS, Azure, GCP, Bedrock, OpenAI, LangGraph, LangSmith, LlamaIndex, Pinecone, Weaviate, Milvus, pgvector 🏢 Description: We're growing our Python AI Engineering team - the group that builds GenAI systems clients actually run, not proof-of-concepts that die after the demo. Expect real agentic/RAG systems, real users, and evaluation data that tells you whether what you built actually works. Depending on fit and timing, you'll be matched to one of several active or upcoming client engagements - each with its own domain, constraints, and stack, but the same bar for engineering quality. You are a good fit for the role if... You default to shipping, not just designing - a proactive, "getting things done" attitude in a fast-paced, client-facing setup where the interesting problems rarely come pre-scoped in a ticket. You own the AI lifecycle end to end - ideation, architecture, delivery, evaluation - and you're comfortable being the technical voice in the room: proposing the approach, defending it with data (evals, latency, cost), and adjusting it when the data says you're wrong. You can translate engineering constraints into terms a client stakeholder acts on - not just "this won't scale" but why, what the tradeoff actually costs them, and what a workable alternative looks like. Good technical judgment shows up as much in how you explain a constraint as in identifying it. You treat client pushback as a design input, not an obstacle - sometimes it reveals a real business constraint you were missing, sometimes it's a shortcut worth resisting. Telling those two cases apart, and being able to argue either side with evidence, is the actual skill. Our expectations 5+ years commercial Python experience, including 3+ years of hands-on GenAI/LLM engineering. Solid engineering fundamentals: building scalable services/APIs (FastAPI, Flask, or Django), writing real test suites (pytest), clean and modular architecture. Hands-on experience building RAG pipelines - retrieval, embeddings, vector search. Working knowledge of agentic patterns: tool-calling, function-calling, multi-step reasoning workflows. Strong prompt engineering skills, including structured outputs (JSON schemas, Pydantic, Instructor or equivalent). Experience with at least one major cloud platform (AWS, Azure, or GCP), including its managed AI/ML services (e.g., Bedrock, Azure OpenAI). An "evals mindset" - you think about relevance, consistency, latency, and cost as real engineering concerns, not afterthoughts. High-proficiency written and spoken English - you'll use it daily with clients and teammates. Welcome Skills Experience with orchestration frameworks beyond the basics - LangGraph, LangSmith, LlamaIndex. Hands-on with a specific vector database (Pinecone, Weaviate, Milvus, pgvector) beyond "I integrated one once." Experience building evaluation frameworks or golden-dataset pipelines specifically (as opposed to just using one). Exposure to data pipeline work feeding AI systems - understanding how data quality/freshness affects model behavior. Prior client-facing / consulting experience in a professional-services or consulting setup. Key tasks Design and build Python services and APIs that wrap LLM-powered functionality. Build and maintain agentic and RAG pipelines: retrieval, reranking, tool-calling, multi-step reasoning, structured outputs. Care about quality beyond "it works" - evals, observability, and the data that tells you when something regresses. Work directly with clients: translate fuzzy business requirements into architecture decisions, and explain your technical tradeoffs to non-technical stakeholders. Work across a distributed, multi-market team and client organizations.

🏢 Summary: The role involves developing and maintaining backend components in Python while supporting the implementation of AI-driven features within products. The position focuses on integrating AI/ML components, working with data, and contributing to simple model development and backend services. It is suited for candidates with commercial Python experience and initial hands-on exposure to AI/ML technologies. 🗂️ Requirements: Minimum 2 years of commercial experience with Python, Hands-on experience with AI/ML features, Experience working with APIs, Experience developing backend services, Understanding of software development best practices 📃 Skills: Python, AI, ML, APIs, TensorFlow, PyTorch, scikit-learn, OpenAI, LLM, NLP, AWS, GCP, Azure 🏢 Description: We are looking for a Junior/Regular AI Engineer to join our team and support the development of AI-powered features within our products. This role is ideal for someone with solid Python skills and initial hands-on experience working with AI/ML components. Responsibilities Develop and maintain backend components using Python Support the implementation of AI-driven features and capabilities within products Collaborate with product and engineering teams to integrate AI solutions Work with data and assist in building simple ML/AI models or integrations Contribute to code quality, testing, and documentation Requirements Minimum 2 years of commercial experience with Python Initial hands-on experience with AI/ML features (e.g. working with models, APIs like OpenAI, basic ML pipelines, etc.) Understanding of software development best practices Experience working with APIs and backend services Good problem-solving skills and willingness to learn in the AI space Nice to have Experience with libraries/frameworks such as: TensorFlow, PyTorch, scikit-learn Familiarity with LLMs, NLP, or Generative AI Experience with cloud platforms (AWS, GCP, Azure) Basic knowledge of data engineering concepts We offer Opportunity to work on real AI-driven products Exposure to modern AI technologies and tools Supportive team and learning environment