AI​/ML Tech Lead

AI/ML Tech Lead #2585 Position Summary

Our partner, one of the U.S.’s most respected healthcare systems, is rethinking how practical machine learning and AI blend within a development-driven environment. As the lead technical contact, you ll help teams apply both AI tooling and ML models to build smarter applications and workflows. You ll provide guidance on data usage, model integration, LLM adoption, and intelligent automation across multiple development groups.

The systems you support are data
-heavy and influence real operational and clinical decisions. You ll prototype, experiment, and introduce practical solutions that improve productivity and insight. This is a hands‑on role with broad visibility and real user impact, ideal for someone who enjoys working at the intersection of AI, machine learning, and engineering.

• BS in Computer Science, Data Science, Information Technology, or related field
• Background in Software Development, AI engineering, or machine learning within complex product environments
• Experience supporting multiple engineering teams or working within large-scale software organizations
• Hands‑on work with both AI tooling (LLMs, copilots) and traditional ML development practices
• Exposure to data-rich systems that support operational or analytical decision‑making
• Familiarity with cloud‑native development environments

• Python
• Machine Learning
• LLMs integration
• AI Tooling
• Java Script
• React / Next.js
• Node.js / NestJS
• Data Analysis & Modeling Concepts
• Model Evaluation
• Algorithmic Thinking
• APIs
• SQL
• Cloud Architecture
• Testing Automation
• System Design
• Version Control
• CI/CD Pipeline

• Guide teams on using AI tooling and ML practices to accelerate development, testing, and research
• Integrate AI‑driven and ML‑driven features into existing and new applications
• Build proofs‑of‑concept showcasing how AI/ML can improve productivity, insight, and decision‑making
• Collaborate with product, data, and engineering leads to shape long‑term AI/ML strategy and roadmaps
• Translate complex AI and ML concepts into practical engineering guidance and workflows
• Ensure AI models, ML pipelines, and automated workflows remain reliable, safe, and scalable
• Contribute to core software and application development when needed
• Review architecture and provide direction on AI/ML‑enabled patterns and best practices
• Drive technical decision‑making around model usage, integration, deployment, and performance
• Promote responsible and ethical approaches to AI and ML adoption within engineering teams
• Support the rollout and evaluation of LLM platforms like Claude and other emerging AI tools