Lead/Staff Full Stack Engineer, AI Platform & Agents

Join us, at Wolters Kluwer, and be part of a dynamic global technology company that makes a difference every day. We’re innovators with impact. We provide expert software and information solutions that the world’s leading professionals rely on, in the moments that matter most. You’ll make a real difference in the lives of millions of people. Together with our customers, we help to advance sustainability, health, justice, prosperity, and commerce around the world. You can thrive at Wolters Kluwer, where diversity is core to our collective strength and high performance. Be your unique self, share your creative ideas, do your best work, and take time to grow in our caring and inclusive culture where you can belong.

About This Role

Our team is building a central GenAI Platform to empower hundreds of product teams across the organization with scalable capabilities for rapid development, validation, and deployment of AI agents. We also drive the development of the most impactful AI agents, ensuring faster delivery and greater impact across multiple domains. With over 20 agents already launched and many more in progress, our work accelerates innovation and improves outcomes in critical industries.

Flagship Agent: UpToDate Expert AI

In Health, we’re launching UpToDate Expert AI - a medical research and clinical reasoning agent that transforms the world’s most widely used point‑of‑care knowledge resource into a real‑time medical assistant. Millions of physicians will rely on it to accelerate differential diagnosis, refine treatment decisions, and reduce cognitive load - while maintaining rigorous safety, privacy, and guideline fidelity. Improvements you ship (latency, reliability, hallucination reduction) will translate directly into faster, higher‑quality patient care at global scale.

What You’ll Do

• Design and implement full‑stack applications, AI agents, and platform components that enable rapid GenAI agent development, validation, and deployment.
• Build developer tooling, CI/CD, and observability for safe, fast iteration (evals, canaries, rollout/rollback, cost and quality telemetry).
• Apply secure SDLC and privacy‑by‑design practices (threat modeling, least privilege).
• Collaborate with product, UX, and domain experts to deliver customer‑focused solutions with measurable outcomes.
• Apply current LLM patterns (RAG, retrieval, routing, tool‑use, evals) to deliver measurable customer value - faster, more reliable AI systems; reduced time‑to‑decision; improved trust/safety metrics; and lower cost per query.
• Lead by example and be heavily hands‑on: drive architecture, mentor engineers, and take ownership of larger projects.

Minimum Qualifications

• 5+ years of professional software engineering experience.
• Strong full‑stack development skills and cloud experience (AWS/Azure/GCP).
• Expert in at least one, and proficient across the others:
• AI Agent development and evaluation
• Backend development
• Frontend development
• Cloud services (AWS/Azure/GCP)
• CI/CD and Infrastructure as Code
• Site Reliability Engineering (SRE)
• Quality engineering / testing strategy
• Secure SDLC and privacy by design
• Proven track record delivering secure, reliable, cloud‑native systems to production.

Nice to have

• Proven ability to deliver software products independently or as part of a small, fast‑paced team.
• Experience of taking AI agents from concept to production, including safety evaluations, iterative testing (e.g., A/B testing), and continuous improvement.
• Experience with LangChain/LangGraph and MCP; vector/RAG systems; OpenSearch.
• Worked on traditional ML tasks like training, deployment, and monitoring.
• Understand how LLMs work, their failure modes, and techniques like fine‑tuning and model adaptation.
• Familiarity with regulatory frameworks such as SOC2, HIPAA, etc.
• Excellent problem‑solving, ownership, and cross‑functional communication.

Stages

• 15-minute intro screen
• 2× live coding in your preferred language
• 1× systems design
• 1× presentation of past work

Timeline

Typically 2-4 weeks.

Your first months

• Month 1: Deep‑dive into one platform component most aligned with your expertise; ship small improvements while ramping up.
• After onboarding: We’ll align on a high‑impact area that fits your strengths and ambitions.