Lead/Staff Full Stack Engineer, AI Platform & Agents (US/Canada Hybrid/Remote)

Wolters Kluwer is building a central GenAI platform and AI agents for healthcare, legal, tax, and compliance applications. The Lead/Staff Full Stack Engineer will design and ship full-stack systems, developer tooling, platform capabilities, and AI agents while improving security, reliability, observability, and customer outcomes.

Responsibilities:

• 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 through writing high-quality, maintainable code that demonstrates engineering craftsmanship

Requirements:

• 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
• Excellent problem‑solving, ownership, and cross‑functional communication
• 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