AI/ML Engineer

🏢 Summary: Senior-level role focused on building AI-driven backend applications using Python, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG). The position involves developing scalable APIs, integrating retrieval pipelines, and deploying services in cloud environments. The role combines advanced AI implementation with solid backend engineering practices. 🗂️ Requirements: 5+ years commercial experience in Python development, Strong backend engineering experience (APIs, services, integrations), Hands-on experience with LLMs, Hands-on experience with RAG implementations, Experience with Azure, AWS, or GCP, Knowledge of software development best practices 📃 Skills: Python, LLMs, RAG, Azure, AWS, GCP, APIs 🏢 Description: Senior Python AI Developer We are looking for an experienced Python Developer to join a team building modern AI-driven applications using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) , alongside standard backend development work. Key Responsibilities Develop and maintain backend services and APIs in Python Build AI-enabled application features using LLMs and RAG workflows Design and integrate retrieval pipelines for contextual AI responses Work with databases, external APIs, and internal systems Deploy and operate services in Azure or other cloud environments Write clean, testable, and scalable production code Requirements 5+ years of commercial experience in Python development Strong background in backend engineering (APIs, services, integrations) Hands-on experience with LLMs and RAG implementations Cloud experience with Azure , AWS, or GCP Solid understanding of software development best practices Tech Stack Python LLMs, RAG Azure (or other cloud platforms)

🏢 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: 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: 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: 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: The offer is for an experienced Python AI Engineer to design and deliver scalable, production-grade AI solutions for a green building design project. The role focuses on building autonomous AI agents, RAG systems, and LLM-powered services, taking solutions from prototype to full production in cloud environments. You will integrate advanced AI workflows into existing ecosystems while ensuring high code quality, evaluation standards, and data reliability. 🗂️ Requirements: 5+ years commercial experience in Python development, Experience building scalable services and APIs using FastAPI, Flask, or Django, Experience writing comprehensive test suites using pytest, Hands-on experience with LangChain, LangGraph, LangSmith, or LlamaIndex, Experience building autonomous AI agents with tool-calling and multi-step reasoning, Practical experience implementing RAG with vector databases, Expert-level prompt engineering with structured outputs (JSON, Pydantic, Instructor), Experience building evaluation frameworks for AI systems, Experience delivering AI applications from prototype to production, Experience building data pipelines for AI systems, Experience with Azure, AWS, or GCP and their managed AI/ML services, Experience integrating LLMs via microservices or workflow engines, High proficiency in English (B2+ level) 📃 Skills: Python, FastAPI, Flask, Django, pytest, LangChain, LangGraph, LangSmith, LlamaIndex, RAG, Pinecone, Weaviate, Milvus, Pgvector, Prompting, JSON, Pydantic, Instructor, Azure, AWS, GCP, Bedrock, Microservices 🏢 Description: About the project Project for a client that promotes green building design. Our expectations Python Expert: 5+ years of commercial experience writing production-quality code. You should be proficient in building scalable services, APIs (FastAPI, Flask, Django), and comprehensive test suites (pytest). Clean Code & Architecture: A strong advocate for clean code principles and the ability to build modular, maintainable software architectures. Hands-on experience with LangChain or other advanced patterns using LangGraph, LangSmith, or LlamaIndex. Agentic Workflows: Proven ability to build autonomous AI agents involving tool-calling, function-calling, and complex multi-step reasoning. RAG & Vector Search: Practical expertise in implementing Retrieval-Augmented Generation (RAG) using vector databases (e.g., Pinecone, Weaviate, Milvus, or Pgvector) and optimizing embedding models. Prompt Engineering Expert-level skill in prompt design, structured outputs (JSON/Pydantic/Instructor). The "Evals" Mindset: You understand that "it works on my machine" isn't enough. You have experience building evaluation frameworks to measure relevance, consistency, latency, cost, and user trust. End-to-End Delivery: A track record of taking AI-powered applications from initial ideation and prototyping through to full-scale production deployment. Data Literacy: Solid understanding of how data quality, freshness, and structure impact model behavior. Experience with building data pipelines for AI consumption. Experience with at least one major cloud platform (Azure, AWS, or GCP), specifically utilizing their managed AI/ML services (e.g., Azure OpenAI, AWS Bedrock). Skills in integrating LLMs into existing product ecosystems via microservices or workflow engines. High-proficiency in written and spoken English (min. B2+ level). Welcome Skills "Getting Things Done" Attitude: A proactive, problem-solving mindset suited for a fast-paced, client-facing environment. Product-First Engineering Mindset: Ownership of the AI lifecycle - from initial ideation and architectural design to production deployment. This includes proactively partnering with stakeholders to refine requirements, identifying the highest-ROI AI patterns, and pivoting quickly based on real-world performance data and user feedback. Stakeholder Communication: The ability to translate complex technical AI concepts into clear business solutions for non-technical stakeholders.

🏢 Summary: Low Code GenAI Developer role focused on designing, fine-tuning, and integrating LLM-based solutions within low-code platforms for biotech use cases. The position involves building agentic systems, RAG pipelines, and chatbots, as well as deploying and optimizing GenAI solutions in enterprise environments. The role requires hands-on experience with cloud infrastructure, MLOps, and enterprise integrations. 🗂️ Requirements: Minimum 2 years of experience in data science or software engineering, Strong experience with Copilot Studio, Strong experience with Power Platform, Deep understanding of LLMs, agents, and foundation models, Experience integrating LLMs into low-code platforms, Experience with vector databases and RAG pipelines, Proficiency in Python, Experience with CI/CD practices, Experience with MLOps and model deployment for LLM systems, Experience with cloud platforms (AWS, GCP, or Azure), Familiarity with SAP and SFDC enterprise systems 📃 Skills: Python, LLM, Agents, RAG, VectorDB, MLOps, AWS, GCP, Azure, SAP, SFDC, PowerPlatform, CopilotStudio, CICD, APIs, Chatbots 🏢 Description: We are looking for a Low Code GenAI Developer to join our project from biotech industry. Responsibilites: Low-code design of GenAI solutions: Design, finetune and evaluate GenAIs solution on proprietary data to meet business needs. Prompt Engineering: Craft, test, and optimize prompts to improve model outputs GenAI Integration: Deploy and integrate LLMs into low-code platforms including agentic solutions, RAG, chatbots, designing APIs, latency/throughput optimization, and monitoring. Research & Experimentation: Stay up-to-date with state-of-the-art LLM architectures and techniques, and evaluate their application for internal use cases. Stakeholder Collaboration: Collaborate with product, data, and engineering teams to translate business problems into LLM-powered solutions and communicate model limitations or trade-offs effectively. Requirements: Minimum 2 years of hands-on data science or software engineering experience We have a strong focus on Copilot Studio and Power Platforms for this role. Deep understanding of with LLMs , agents and foundation models Familiarity with enterprise applications like SAP, SFDC and their AI offerings Proficiency in Python and CI/CD practices Familiarity with vector databases and retrieval-augmented generation (RAG) pipelines Experience with MLOps, cloud infrastructure (AWS/GCP/Azure), and model deployment tools for LLM-based systems. English - min B2 What we offer: Deep understanding of the domain knowledge in the field of molecular biology and biotechnology Exposure and hands on experience in the development of modern cloud data solutions Ability to deliver AI and ML solutions at scale Opportunity to work in an experienced and engaged team consisting of Data Engineers, Data Scientists, Machine Learning Engineers and stakeholders from different business domains