Senior AI Engineer (R&D) | Life Sciences

Who we are ADC is a boutique Data and AI consultancy with offices in the Netherlands, Sweden, and Denmark, and an award-winning data design studio (CLEVER°FRANKE) embedded in the group. We help data-driven leaders in complex, high-stakes environments move fast without losing control. We deliver four core outcomes: growing revenue and margin, moving from AI pilots to scale, reducing risk while staying confidently compliant, and building data products people love to use. We are a collective of statisticians, designers, scientist s , and ML/AI engineers with deep expertise in the industries we serve: financial services, life sciences, healthcare, the public sector, retail, agriculture, and transportation. We do not arrive with generic tools and a slide deck. We walk in, work alongside the people who know the organisation best, and build solutions that actually run. We call ourselves Wayfinders. Our clients trust us to navigate complexity and make it real: turning ambition into action in environments where the margin for error is small and the consequences of getting it wrong are real, for customers, employees, supply chains, and the bottom line. Shape the Future of Agentic AI in Life Sciences R&D with ADC Our Engineering & Analytics tea m designs and builds agentic AI systems for data-heavy, often regulated environments, from document-intensive processes to complex internal workflows and analytics. We are looking for a Senior AI Engineer who can take an agentic solution from idea to production independently - shaping the architecture with clients and owning the implementation end to end, from agent logic and backend services to cloud infrastructure and deployment. You’ll work closely with client engineering teams, product owners, business managers, and senior leaders, so you need to be able to explain technical decisions clearly and effectively to both technical and non-technical audiences. Where you'll make an impact Agentic System Design & Build • Design and build agentic workflows, including multi-step agents, tool use, orchestration, memory, and human-in-the-loop checkpoints. • Build skills/plugins and connectors that give agents safe, well-scoped access to client systems and data; review platform tools and connectors and recommend what to enable or disable based on security, data protection, and research value. • Build MCP connectors and skills linking the assistant to client systems such as on-prem data lakes and HPC clusters. • Own solutions end to end: architecture, implementation, deployment, monitoring, and handover, working side by side with client’s IT, engineering, scientific teams, and subject matter experts to deliver well-documented, maintainable components. • Build the backend around the agents: APIs, data pipelines, and integrations with client systems. RAG, Guardrails & Governance • Design, build, and tune RAG pipelines end to end, from ingestion and chunking to embeddings, search, reranking, generation, and evaluation. • Design and implement AI guardrails and governance layers, including input and output filtering, policy enforcement, PII handling, and audit trails. • Set up and operate AI gateways that manage model routing, access control, rate limits, cost tracking, and logging across providers and teams. • Build evaluation and observability into every agent system so quality can be measured, not just assumed. Cloud & Delivery • Set up and manage cloud infrastructure, CI/CD, and environments, with security and compliance in mind. • Contribute to reusable patterns and reference architectures that the team can apply across engagements. Client Engagement • Take part in client workshops, scoping, and technical discovery, turning business problems into workable designs. • Share knowledge internally