Lead/Staff Full Stack Engineer, AI Platform & Agents
Role details
Job location
Tech stack
Job description
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.
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.
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.
Benefits & conditions
-
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.
Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.