Data and AI Engineer - RDT Pharma R&D
AI in this role
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
The Principal Data and AI Engineer leads the architectural design, implementation, and optimization of enterprise-grade data infrastructure and advanced AI solutions (including agentic workflows and RAG systems). In this role, you will modernize clinical data generation and submissions content through automated, high-accuracy multi-agent systems and scalable cloud infrastructure. You will establish engineering best practices, guide cross-functional teams, and liaise with senior business leadership to turn complex data and AI strategies into production-ready platforms.
Description of the area
The Clinical Submission Data and Content Generation & Reuse function focuses on transforming the creation, management, and reuse of clinical data and regulatory submission content through advanced digital capabilities and AI-driven automation. The organization develops structured content management platforms, reusable data and content assets, and intelligent agent ecosystems that support clinical submissions, regulatory compliance, and scientific communication.
The team is at the forefront of leveraging Generative AI, Agentic Workflows, Knowledge Graphs, and advanced analytics to improve quality, accelerate submission timelines, and enable data-driven decision-making across the clinical and regulatory landscape.
Job Responsibilities
Scope / Content Leadership:
Leads the design and construction of large-scale data architectures. Oversees and mentors junior engineers while driving technical excellence and setting standards across the team.
Design and deploy high-performance AI systems, RAG platforms, and multi-agent workflows.
Architect scalable distributed data systems, cloud data platforms, and advanced ETL/ELT processing pipelines to manage enterprise-level clinical data.
Accountability/Problem Solving:
Solves highly complex data engineering problems and troubleshoots intricate performance issues.
Ensures high resilience, compliance, and strict security standards across all data infrastructure and AI product deployments.
Stakeholder Management:
Liaises with senior leadership to align on data and AI strategy and functional needs, and occasionally step into product-owner-level alignment when needed.
Guides and directs various technical teams and effectively influences the team's architectural decisions.
Influence key stakeholders on AI strategy, roadmap prioritization, and solution adoption.
Impact/Strategy:
Define and execute the technical roadmap for data platforms, Generative AI, and agentic AI capabilities within the function.
Lead high-impact projects that directly influence organizational objectives, innovation initiatives, and operational efficiency.
Establish strict software engineering standards by integrating automated evaluation, test-driven development (TDD), and robust testing frameworks into CI/CD pipelines.
Complexity / Product Size:
Manages enterprise-level data systems and infrastructure.
Designs and implements advanced data processing frameworks, scalable data warehouses, and data lakes.
Work with large-scale clinical, regulatory, and enterprise datasets across structured and unstructured formats.
Business / Technical ability:
Apply strong software engineering principles to develop secure, scalable, and maintainable AI products.
Acts as an expert in multiple programming languages and Big Data technologies.
Demonstrates a deep understanding of distributed systems, cloud data platforms, advanced ETL/ELT patterns, and data architecture best practices.
Balance system accuracy with inference latency and API costs by establishing LLM evaluation benchmarks, token-optimization strategies, model distillation, and cloud FinOps principles.
Qualifications
Education / Experience
Extensive track record leading large-scale data architecture and AI/ML solution deployments in complex environments.
Proven track record of leading high-impact projects that shape enterprise-level data infrastructure and developing long-term data and AI architecture strategies.
Proven experience optimizing end-to-end latency and retrieval accuracy in production RAG systems or agentic AI solutions.
Demonstrated experience contributing significantly to data governance and ensuring the resilience and security of critical data infrastructure.
Demonstrated leadership in embedding agile software engineering standards, CI/CD automation, and MLOps/LLMOps frameworks.
Technical Skills
AI/LLM Stack: Graph-based multi-agent systems, RAG optimization (vector & keyword hybrid search), MCP servers, and LLM evaluation frameworks.
Data & Cloud Engineering: AWS, distributed computing, workflow orchestration, microservices, and REST APIs.
Core Engineering: Advanced programming proficiency, distributed data systems, ETL/ELT pipelines, and GitLab CI/CD integration.
Familiarity with clinical and regulatory data standards (e.g., CDISC SDTM/ADaM, FHIR, OMOP) and integration patterns with enterprise content/RIM platforms (e.g., Veeva Vault, EDMS).
Additional Qualifications
Ability to interface directly with senior business leadership to translate business requirements into technical roadmaps and architectural decisions.
Proven capability to mentor junior and mid-level engineers, promote strong engineering cultures, and drive technical decision-making.
Strong strategic mindset with the ability to influence cross-functional architectural decisions and drive technical excellence.
Compensation & Benefits
The expected salary range for this position, based on the primary location of Warsaw Grafit is 228,900.00 PLN - 425,100.00 PLN. Final compensation will be determined by a number of factors, including your skills, experience, qualifications, and location. In addition to base pay, this role may be eligible for a discretionary annual bonus with a target of 20% subject to both individual and company performance.This position also offers an attractive benefits package.
Learn more about how we reward our employees at Roche.
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.
How we score this
Data and AI Engineer - RDT Pharma R&D at Roche scores 67 out of 100 on AI centrality, which makes it AI Level 3 of 4 (Works on AI) on this board. The level measures how much of the work is AI, not seniority.
AI Level 3. The daily work is on or around AI systems, without necessarily building the model: remove AI and the job is hollow.
- AI Level 480 to 100
- AI Level 360 to 79
- AI Level 240 to 59
- AI Level 10 to 39
Bands come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
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A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- How do you decide that one model's output is better than another's for a given task?
- Describe a typical day in a role like this one: which parts run through AI directly?
Adapt your resume
- List these exact terms on your resume: Rag, AI Agents, Ml Ops, and AI Evaluation. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.
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