As Lead/Staff Full Stack Engineer, AI Platform & Agents, you will Build the GenAI platform that powers critical decisions in healthcare, legal, tax,
and compliance industries. Your work will directly shape the future of these
fields, enabling faster, safer, and more impactful decision-making at a global
scale.
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.
You'll join a 100-engineer remote-first team within a larger organization that
combines the stability of an established company with the agility of a startup. In
this high-autonomy, high-impact role, you'll take problems from concept to
production, mentor less experienced engineers, and lead by example. You'll
design and ship full-stack systems, shape platform capabilities to empower
hundreds of product teams, and directly contribute to the development of the
most impactful AI agents.
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.
Tech stack
You don’t need to know all of these on day one, but you should be ready to learn
quickly.
TypeScript, Node.js, React, Python, LangChain/LangGraph, MCP/A2A, Rust
AWS (primary), Azure, GCP; Docker, Terraform, GitHub Actions
DocumentDB, DynamoDB, OpenSearch, Azure AI Search
Azure OpenAI, AWS Anthropic, Google Gemini
GitHub, Confluence, Slack
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.
Team context
Org and Sub-teams: Central GenAI Platform within Wolters Kluwer, driving
innovation across businesses by creating re-usable platform services and
components. Sub-teams are fewer than 10 engineers, focused on platform
services or customer-facing agents.
Culture and Reporting: We value a "manager of one" mindset, where
outcomes matter more than optics. Authority is earned through
demonstrated impact, not tenure or title. You’ll report directly to the
Director of Engineering, AI Platform.
Team Size and Impact: Our globally distributed team of ~100 engineers
combines the stability of an established company with the agility of a
startup. We are moving fast, and there are many areas where you can have
a big impact.
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.
Excellent problem-solving, ownership, and cross-functional
communication.
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.
Stages
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.
To apply:
Please submit your resume along with a brief cover letter that includes a “Statement of Exceptional Work.” In your cover letter, highlight one of your most impactful projects by addressing the following:
Your role and the problem space you were working in
The technical and product challenges you faced, and how you addressed them
The measurable impact of your work (e.g., metrics, outcomes, improvements)
This will help us better understand your approach to solving complex problems and the value you bring to the team.
Please do not include any proprietary or confidential information in your submission
#BETHEDIFFERENCE
