AI Engineer

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections,  where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.

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The Position

We are seeking an experienced AI Engineer to join Roche Information Solutions (RIS), bringing strong expertise in designing, building, and optimizing production-ready AI, Generative AI and multi-agent ecosystems. This role combines hands-on model development and orchestration with responsibilities for system architecture, prompt engineering, and ensuring highly scalable, secure solutions.

You will work closely with business stakeholders to understand requirements and deliver reliable AI solutions/workflows that leverage proprietary enterprise datasets. The role requires ensuring that all AI systems are built securely and remain fully compliant with global data privacy and governance regulations, handling sensitive healthcare data in alignment with industry standards such as HIPAA and GDPR.

Success in this role requires proven experience with machine learning, cloud platforms, and modern AI frameworks. You will be expected to build robust agentic systems, implement retrieval-augmented generation (RAG) pipelines, ML algorithms, and champion secure, explainable AI engineering best practices within a healthcare technology environment.

REQUIRED EXPERIENCE, SKILLS & QUALIFICATIONS

• 5–8 years of experience in ML/NLP, applied AI, data engineering, or a related technical field, including at least 2–3 years of hands-on experience developing Generative AI or LLM-based solutions.
• Practical experience with NLP, transformer-based models, and LLM application development using PyTorch, TensorFlow, or Hugging Face Transformers.
• Hands-on experience with Generative AI application and agent-orchestration frameworks such as LangChain, LangGraph, LlamaIndex, or CrewAI.
• Experience with vector databases and vector search technologies such as Pinecone, Chroma, or PostgreSQL with pgvector.
• Strong proficiency in Python and SQL, with experience preparing and transforming data for model development, evaluation, and production AI workflows.
• Experience building retrieval pipelines involving document chunking, embedding generation, vector indexing, metadata filtering, similarity search, and result reranking.
• Strong prompt and context engineering skills, including structured-output design, tool calling, and grounding responses in approved data sources.
• Experience implementing input and output guardrails, fallback mechanisms, automated evaluations, and regression tests for LLM applications.
• Expertise in deploying and operating production AI/ML and Generative AI solutions on AWS or another major cloud platform, including monitoring, scaling, security, reliability, and cost optimization.
• Proficiency in Git, Docker, automated CI/CD pipelines, unit and integration testing, REST API development, and microservices architecture.
• Experience with LLM observability platforms such as Langfuse or LangSmith to trace and monitor model calls, agent workflows, tool usage, retrieval steps, errors, latency, and cost.
• Experience working in SAFe/scaled Agile environments.
• Demonstrated ability to collaborate across product, engineering, data, cybersecurity, privacy, quality, regulatory, and domain teams while identifying risks and dependencies early.
• Strong analytical, written, and verbal communication skills, with the ability to explain technical trade-offs clearly to technical and non-technical stakeholders.
• Experience applying security-by-design and privacy-by-design principles to AI systems handling sensitive data, including least-privilege access controls, encryption in transit and at rest, secrets management, audit logging, data minimization, and secure access to model endpoints.

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KEY RESPONSIBILITIES

• Design, build, and optimize production AI/ML systems, including NLP applications and single and multi-agent workflows. Implement routing, intent classification, planning, tool use, state and memory management, error handling, and human-in-the-loop(HITL) escalation where required.
• Architect and implement production AI systems using ap…