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.

At Roche we are committed to delivering greater benefits to our patients by applying digital data and AI capabilities to real-world operational challenges. The AI Engineer is a senior hands-on technical contributor within the AI Engineering Cluster responsible for designing delivering evaluating and operating robust AI solutions across Pharma Technical Operations.

This role owns meaningful parts of end-to-end AI delivery including solution architecture data pipelines machine learning and GenAI systems agentic workflows AI evaluation harnesses backend services user interfaces and production operations. The role partners with business IT quality and technical experts to translate complex operational challenges into reliable scalable compliant and measurable AI-enabled products.

The role is a senior AI engineering role for someone who combines strong technical depth with practical delivery ownership. The AI Engineer is expected to independently lead solution delivery make sound architectural decisions mentor other engineers and ensure AI solutions create measurable business value while meeting reliability scalability evaluation agent management and compliance expectations.

• End-to-end delivery ownership: Own the technical delivery of AI solutions from problem understanding and design through build deployment monitoring evaluation adoption and continuous improvement.
• Solution architecture: Design scalable AI application architectures including data flows model integration GenAI orchestration APIs user interfaces agent workflows and production infrastructure.
• GenAI and agentic AI: Design and implement advanced GenAI solutions including RAG architectures multi-step workflows tool-using agents LLM orchestration and evaluation approaches.
• Engineering quality: Apply and help evolve strong engineering practices including automated testing code reviews CI/CD observability documentation and maintainability.
• Production performance: Ensure AI solutions are reliable secure monitored cost-aware and continuously improved in production or production-like environments.
• Value delivery: Translate business needs into technical solutions and help define success metrics that demonstrate operational and business impact.

• Evaluation framework design: Design and implement data science-driven evaluation frameworks for AI GenAI RAG and agentic AI systems including offline benchmarks golden datasets synthetic test cases human evaluation workflows and automated regression testing.
• AI harness development: Own the development of AI engineering harnesses for repeatable experimentation evaluation validation and comparison of models prompts retrieval strategies agent behaviors and tool integrations.
• Agentic AI solution ownership: Design and deliver production-grade agentic AI systems including agent orchestration tool integration task decomposition state handling memory patterns guardrails and human escalation paths.
• Agent lifecycle management: Manage deployed agents across their lifecycle including versioning testing release management monitoring incident response continuous improvement and retirement.
• Performance and risk monitoring: Implement and operate observability for AI systems including quality metrics business impact metrics cost latency failure modes drift hallucination risk grounding quality and operational reliability.
• Model and workflow comparison: Lead structured experiments to compare foundation models prompts embeddings retrieval approaches orchestration patterns and agent configurations.

• AI engineering depth: Apply strong knowledge of machine learning predictive modeling time-series computer vision and multimodal AI as well as LLMs RAG agentic AI prompt engineering model evaluation and AI application design.
• Software and platform engineering: Build robust Python-based services APIs microservices dashboards and integrations using modern software engineering practices.
• Cloud and DevOps: Design and deploy solutions using AWS Docker CI/CD pipelines monitoring and cloud or hybrid deployment patterns.
• Data engineering: Work with SQL operational data time-series data structured and unstructured data and data pipelines required for AI products.
• Evaluation methods: Use quantitative metrics qualitative review human-in-the-loop assessment statistical comparison and production feedback loops to make engineering decisions.
• Regulated delivery: Partner with IT and quality stakeholders to ensure solutions are designed with appropriate compliance validation security privacy data governance responsible AI traceability documentation and lifecycle controls.

• Engineering mentorship: Coach less experienced engineers on AI engineering software development evaluation practices agentic systems cloud deployment and production operations.
• Best practices: Contribute to shared standards reusable patterns and engineering guidelines for the AI Engineering Cluster.
• Technical problem-solving: Structure ambiguous technical problems evaluate solution options and guide teams toward pragmatic high-impact outcomes.
• Capability building: Help build AI engineering capability across Pharma Technical Operations through knowledge sharing examples and hands-on support.

• Stakeholder partnership: Partner with process experts business stakeholders IT quality and other digital teams to identify design implement evaluate and sustain AI-enabled improvements.
• Organizational alignment: Communicate technical concepts clearly to both technical and non-technical audiences supporting alignment and adoption.
• AI advocacy: Promote effective responsible and value-driven use of AI within Pharma Technical Operations.
• Continuous learning: Stay current with advances in AI GenAI agentic systems evaluation science software engineering and cloud technologies and translate relevant innovations into practical Roche use cases.

• Bachelors degree in computer science data science engineering mathematics or a related field; or equivalent practical experience.
• Demonstrated track record independently delivering production-grade software AI machine learning data or analytics solutions.
• Strong hands-on Python expertise and solid knowledge of software engineering practices.
• Experience designing and delivering cloud-based or hybrid applications preferably using AWS.
• Hands-on experience with modern agent frameworks and harnesses such as DeepAgents LangGraph LangChain or equivalent technologies.
• Strong understanding of LLMs RAG prompt engineering AI agents model integration tool use and evaluation approaches.
• Experience with APIs microservices CI/CD Docker monitoring automated testing code review practices and AI evaluation harnesses.
• Ability to translate evaluation results into engineering decisions product improvements and stakeholder recommendations.
• Ability to lead technical delivery manage ambiguity and influence cross-functional stakeholders.
• Experience in industrial manufacturing pharmaceutical biotechnology quality or regulated environments is strongly preferred.

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.

* This is an on-site/hybrid position and no relocation assistance is provided.

A healthier future drives us to innovate. Together more than 100000 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.

Lets build a healthier future together.

Roche is an Equal Opportunity Employer.