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

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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 behaviors.

Collaboration: Ability to thrive in a fast-paced, product-focused agile engineering environment alongside data scientists and product owners.

Communication: Strong technical writing and communication skills for documenting complex system architectures and cross-functional collaboration.

Bonus: Domain & Regulatory Knowledge

Experience or strong familiarity with the following clinical trial standards will give you a significant advantage:

Clinical Document Automation: Automating the full clinical document generation workflow, specifically translating protocols to Clinical Study Reports ( Protocol → CSR ).

Data Lineage Workflows: Experience transforming raw clinical data and statistical outputs into structured regulatory documents ( SDTM/ADaM/ARD → TLG → CSR ).

CDISC Standards: Deep understanding of CDASH (CRFs), SDTM, ADaM, ARD/ARM, and Define-XML.

Regulatory Submissions: Familiarity with ICH M11 (protocol/SoA), ICH E3 (CSR), and eCTD Module 5.

Compliance Frameworks: Designing software to align strictly with GxP , 21 CFR Part 11 , and ICH guidelines, ensuring total auditability, traceability, and explainability.

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Who we are

A healthier future drives us to innovate. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.

Let’s build a healthier future, together.

Roche is an Equal Opportunity Employer.