Automation Engineer Submission Data and Content Generation & Reuse (AIDCG) - Pharma R&D — Roche

Full description

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.

The Position About the Role We are hiring a GenAI Agent Developer to join the AIDCG initiative, a transformative program focused on automating complex, multi-step document generation workflows through state-of-the-art AI agents. This role blends advanced GenAI engineering, open-source innovation, and AWS cloud expertise to deliver production-grade AI agents that are accurate, scalable, and highly secure. You will design orchestration frameworks that transform raw, unstructured data into highly structured, compliant outputs.

Description of the area Job Responsibilities

• Agentic Architecture: Design, deploy, and scale multi-agent orchestration systems and autonomous workflows using cutting-edge frameworks.
• Advanced RAG Pipelines: Build and optimize advanced retrieval-augmented generation (RAG) pipelines over massive, heterogeneous datasets (structured and unstructured).
• State & Memory Management: Implement robust state management, short/long-term memory systems, and self-correction/reflection loops within agent networks.
• Evaluation & Guardrails: Create and implement robust evaluation metrics, observability pipelines, and guardrails for content quality, hallucination reduction, bias mitigation, and safety standards.
• Performance Optimization: Monitor and optimize AI inference cost, latency, throughput, token usage, and overall system reliability.
• Security & Access Control: Implement robust access controls, data encryption, user authentication, and prompt injection mitigation across all LLM workflows.
• Collaboration & Best Practices: Document and share reusable agent patterns, prompt libraries, and engineering components across cross-functional technical teams.

Qualifications Education / Experience

• Demonstrated experience taking ownership of ambiguous tasks, successfully driving small to medium initiatives, and acting as a technical mentor.
• Proven track record of engaging in knowledge-sharing initiatives, speaking at internal technical events, and navigating group dynamics in diversified settings.

Technical Skills

• GenAI Development: Advanced prompt engineering, fine-tuning, RAG/GraphRAG, schema-constrained outputs, function/tool-calling, and semantic caching.
• Agentic Frameworks: LangChain, LangGraph, LlamaIndex, CrewAI , AutoGen , and DSPy .
• Vector Databases: FAISS, Milvus, Qdrant, Pinecone, Weaviate, and Pgvector .
• LLM Serving & Infra: vLLM, Hugging Face TGI, and Ollama (for local development).
• Guardrails & Validation: NeMo Guardrails, Guardrails AI, Pydantic, and Instructor .
• LLM Ops & Observability: Ragas, DeepEval, Langfuse, LangSmith , and Phoenix.
• Data Parsing: Unstructured, Apache Tika, LlamaParse , and PDFPlumber.
• Programming & DevOps: Python (FastAPI, asyncio), REST/GraphQL APIs, Git, CI/CD pipelines, Docker, Kubernetes, and OpenTelemetry.
• Emerging Protocols: Model Context Protocol (MCP) and Agent2Agent communication standards.

AWS Ecosystem (Baseline Experience)

• Amazon Bedrock: Foundation model access, custom configurations, and managed agent workflows.
• Amazon SageMaker: Fine-tuning, hosting, evaluating, and deploying open-source LLMs.
• Amazon OpenSearch Service: Vector search, hybrid search, and enterprise retrieval infrastructure.
• AWS Step Functions: Multi-step orchestration and state machine management for hybrid AI/traditional pipelines.

Additional Qualifications

• Problem-Solving: An analytical mindset capable of breaking down highly abstract, ambiguous logic loops into predictable agent behavior