Level

Wolters Kluwer

Senior Full Stack Engineer, AI Platform & Agents

AI in this role

openaianthropicgeminilangchainlanggraphazure-openai
ragai-agents

As principal 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. 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 through writing high-quality, maintainable code that demonstrates engineering craftsmanship

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 VP 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.
Work setup: Remote-first in US or EU, with hybrid options near major
offices. Collaboration requires 9–11 AM CST overlap. Occasional travel for
team onsites/offsites as needed.

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

  • 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.

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

Our Interview Practices

To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.

Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.

How we rate this

Senior Full Stack Engineer, AI Platform & Agents at Wolters Kluwer rates 91 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

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

RAGAI AgentsOpenAIAnthropicGeminiLangChainLangGraphAzure OpenAI

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you decide when an AI agent can act on its own versus asking for approval first?
  3. What are the limits of OpenAI that you've run into, and how did you work around them?
  4. What's a project where you used Anthropic hands-on?
  5. Walk me through how you've used Gemini in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: RAG, AI Agents, OpenAI, Anthropic, and Gemini. 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.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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