Digital R&D Principal Engineer

About Sanofi

Sanofi is a global biopharmaceutical company dedicated to chasing the miracles of science to improve people's lives. Our Digital R&D Software Engineering team sits at the intersection of cutting-edge technology and life-changing medicine — building the digital backbone that accelerates drug discovery, clinical development, and patient outcomes. We are guided by our values: Aim Higher, Act for Patients, Be Bold, and Lead Together.

Position Summary

We are looking for a talented and driven AI Software Engineer – Agentic Solutions to join Sanofi's Digital R&D Software Engineering team. In this mid-level role, you will design, develop, and deploy agentic AI systems that autonomously reason, plan, and act to solve complex challenges across pharmaceutical research and development.

You will work at the forefront of applied AI — building multi-agent architectures, LLM-powered workflows, and intelligent automation pipelines that directly support Sanofi's mission to bring life-saving therapies to patients faster. This includes hands-on experience with platforms such as AWS Bedrock and AWS AgentCore for scalable agent deployment, and MCP (Model Context Protocol) for standardized tool and data source integration. This is a high-impact role for an engineer who is passionate about autonomous AI systems and wants to apply them in a meaningful, regulated, and scientifically rigorous environment.

Key Responsibilities

🤖 Agentic AI Design & Development

• Design, build, and deploy autonomous AI agent systems capable of multi-step reasoning, planning, and task execution across R&D workflows

• Develop multi-agent architectures where specialized agents collaborate, delegate, and coordinate to solve complex pharmaceutical problems

• Implement tool-using agents that interact with APIs, databases, internal systems, and external data sources

• Build feedback loops and self-correction mechanisms to improve agent reliability and accuracy over time

• Leverage AWS AgentCore for agent lifecycle management, memory persistence, and tool integration — deploying and managing agents at scale in a governed, enterprise environment

• Design and implement MCP (Model Context Protocol)-based agent architectures, building MCP servers and clients to standardize how agents interact with external tools, data sources, and Sanofi's internal services

🧠 LLM-Based Solution Engineering

• Develop and fine-tune LLM-based applications using frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, or similar

• Design and implement Retrieval-Augmented Generation (RAG) pipelines to ground agents in Sanofi's proprietary scientific and operational knowledge

• Engineer prompt engineering strategies, chain-of-thought reasoning, and structured output parsing for production-grade reliability

• Evaluate and benchmark LLM performance across models (GPT-4, Claude, Mistral, Llama, etc.) for specific pharmaceutical use cases

• Build and deploy agentic solutions using AWS Bedrock Agents and Bedrock Knowledge Bases, leveraging foundation models available on Bedrock (e.g., Claude, Titan, Llama) for scalable, managed generative AI workloads

🏗️ Software Engineering & Architecture

• Write clean, maintainable, production-ready Python code following software engineering best practices (SOLID principles, design patterns, code reviews)

• Build RESTful and event-driven APIs to expose agent capabilities to downstream applications and users

• Implement CI/CD pipelines, automated testing (unit, integration, regression), and monitoring for AI systems

• Ensure observability of agent behavior through logging, tracing, and evaluation frameworks (e.g., LangSmith, Arize, Weights & Biases)

🔗 Systems Integration

• Integrate agentic solutions with Sanofi's existing digital ecosystem including data platforms, clinical systems, ERP, and knowledge management tools

• Connect agents to vector databases (Pinecone, Weaviate, pgvector, Chroma) for semantic search and memory management

• Work with cloud-native services ( AWS) to deploy scalable, secure, and compliant AI workloads — including AWS Bedrock managed AI services for foundation model access and agent orchestration

• Integrate MCP-compatible tools into agent workflows, enabling standardized, interoperable connections between AI models and external data sources, APIs, and enterprise systems

• Collaborate with data engineers to ensure agents have access to high-quality, governed data pipelines

🤝 Collaboration & Delivery

• Partner with product managers, data scientists, and domain experts (biologists, clinicians, regulatory specialists) to translate scientific needs into agentic AI solutions

• Participate in Agile ceremonies (sprint planning, retrospectives, demos) and contribute to team velocity

• Contribute to technical documentation, architecture decision records (ADRs), and internal knowledge sharing

• Mentor junior engineers and contribute to the team's AI engineering standards and best practices

Required Qualifications

Technical Skills

• Programming: Strong proficiency in Python (3.9+); familiarity with async programming, type hints, and packaging

• AI/ML Frameworks: Hands-on experience with LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or equivalent agentic frameworks

• LLMs & Generative AI: Practical experience building applications on top of OpenAI, Anthropic Claude, or open-source LLMs

• AWS Bedrock: Hands-on experience with AWS Bedrock as a managed generative AI platform — including working with Bedrock Agents for orchestrating multi-step agentic workflows, Bedrock Knowledge Bases for RAG-based retrieval, and selecting and evaluating foundation models available on Bedrock (e.g., Anthropic Claude, Amazon Titan, Meta Llama)

• AWS AgentCore: Experience using AWS AgentCore for building, deploying, and managing AI agents at scale — including agent lifecycle management, memory configuration, and tool/API integration within the AgentCore framework

• MCP (Model Context Protocol): Practical experience with Anthropic's Model Context Protocol — including building MCP servers and clients, integrating MCP-compatible tools into agent workflows, and designing MCP-based agent architectures that standardize how AI models interact with external data sources and services

• RAG Systems: Experience designing and implementing end-to-end RAG pipelines including chunking strategies, embedding models, and retrieval optimization

• Vector Databases: Working knowledge of at least one vector store (Pinecone, Weaviate, Chroma, pgvector, Qdrant)

• APIs & Integration: Experience building and consuming REST APIs; familiarity with OAuth2, API gateways, and webhook patterns

• Cloud Platforms: Hands-on experience with, AWS,  — including managed AI services (Azure OpenAI Service, AWS Bedrock, Vertex AI)

• DevOps & MLOps: Familiarity with Docker, Kubernetes, GitHub Actions, and model deployment pipelines

• Data Handling: Proficiency with SQL, JSON, and working with structured/unstructured data sources

Domain & Engineering Practices

• Demonstrated experience building agentic or autonomous AI systems in a professional setting

• Understanding of agent memory models (short-term, long-term, episodic), tool use, and planning strategies (ReAct, Plan-and-Execute, etc.)

• Solid grasp of software engineering fundamentals: version control (Git), testing, code review, and documentation

• Awareness of AI safety, responsible AI principles, and the importance of human-in-the-loop design in regulated environments

Preferred Qualifications

• Experience working in pharmaceutical, biotech, healthcare, or other regulated industries

• Familiarity with GxP compliance principles and how they apply to AI/software systems

• Advanced experience with AWS Bedrock — including custom model evaluation, guardrails configuration, and Bedrock Studio for rapid prototyping of agentic workflows

• Experience designing MCP server implementations for proprietary enterprise data sources, enabling AI agents to securely and consistently access internal knowledge systems via the Model Context Protocol

• Deeper expertise with AWS AgentCore — including advanced memory management patterns, multi-agent orchestration, and integrating AgentCore-managed agents with enterprise identity and access management (IAM) controls

• Experience with knowledge graphs or ontologies in scientific domains

• Exposure to fine-tuning or RLHF workflows for domain-specific LLM adaptation

• Contributions to open-source AI projects or published technical writing

• Experience with multi-modal AI (text + image, text + structured data)

• Knowledge of clinical data standards (CDISC, HL7 FHIR) or scientific data formats

Education Requirements

• Bachelor's degree in computer science, Software Engineering, Artificial Intelligence, Data Science, or a related technical field (required)

• Master's degree in a relevant discipline (preferred)

• Equivalent practical experience with a strong portfolio of AI engineering work will be considered in lieu of formal education

Experience Level

• 1–3 years of hands-on experience building AI/ML or LLM-based applications

• At least 1 year of direct experience with agentic AI systems, autonomous workflows, or multi-agent frameworks

• A track record of delivering production-grade AI features end-to-end, from design through deployment and monitoring

Key Competencies

Description

🎯 Technical Excellence

Writes high-quality, testable, and maintainable code; makes sound architectural decisions including selection of the right agentic platform (Bedrock, AgentCore, MCP) for the use case

🔍 Problem Solving

Breaks down ambiguous, complex problems into structured, executable solutions

🚀 Innovation Mindset

Proactively explores emerging AI techniques — including evolving agent protocols like MCP and managed platforms like AWS Bedrock — and applies them pragmatically

🤝 Collaboration

Works effectively across disciplines — engineering, science, product, and business

📣 Communication

Clearly articulates technical concepts to both technical and non-technical stakeholders

⚖️ Responsible AI

Considers ethical implications, bias, safety, and compliance in every design decision

📈 Ownership

Takes end-to-end accountability for features from ideation to production

What We Offer

At Sanofi, we believe that people who chase the miracles of science deserve extraordinary support. Joining our Digital R&D Software Engineering team means:

• 🌍 Global Impact — Your code will directly accelerate drug discovery and improve patient outcomes worldwide

• 🧬 Cutting-Edge Work — Access to state-of-the-art AI infrastructure, enterprise LLM platforms, and rich scientific datasets

• 📚 Continuous Learning — Dedicated learning budgets, access to AI research communities, and internal tech talks

• 🤝 Inclusive Culture — A diverse, global team united by the mission to Act for Patients

• 💼 Competitive Compensation — Market-aligned salary, performance bonuses, and comprehensive benefits

• 🏡 Flexible Working — Hybrid work model with flexibility to balance deep work and collaboration

• 🌱 Career Growth — Clear progression paths within Sanofi's engineering career framework, with opportunities to grow into L3-2 and beyond

• 🏥 Health & Wellbeing — Comprehensive health coverage, mental wellness programs, and employee assistance resources