Senior AI Engineer Lead

AI Pod Team Lead – GenAI & RAG Solutions Remote (US-Based) | 12+ Months (Extension Likely) | Full-Time, 40 hrs/week Overview We are partnering with a Fortune 500 healthcare organization that is accelerating AI delivery across its business units. They are building a dedicated AI Pod team focused on Generative AI (GenAI) and Retrieval-Augmented Generation (RAG) use cases, with more than 90 opportunities identified on GCP alone. This is a hands-on leadership role where you will shape end-to-end AI solutions, drive prioritization of use cases, and deliver measurable value in fast-paced, agile increments. What You’ll Do • Lead a 5-person pod to design, build, and deploy AI Agents, Chatbots, and Assistants using frameworks like LangChain, LangGraph, CrewAI, and Google ADK. • Implement RAG architecture, observability, and responsible AI practices. • Deliver full-stack solutions (Python, JS/TS, React, Node.js). • Ensure CI/CD, infrastructure as code (Terraform), and monitoring are in place. • Work closely with business stakeholders to prioritize and align delivery with use cases. • Drive integrations across enterprise platforms (Salesforce, Workday, ServiceNow, O365, Gleam). • Take proof-of-concepts to production-ready solutions. • Bring an end-to-end perspective, integrating all components into a unified solution rather than leaving each engineer to solve only their part. What We’re Looking For • Hands-on leader with the ability to both guide the team and dive deep into coding/architecture. • Strong experience in Python, CI/CD (GitHub/GitLab), and AI/ML frameworks. • Familiarity with LLMs/APIs such as Gemini, Claude, OpenAI, or Llama; and vector databases (Pinecone, AlloyDB, PGVector). • Background in building and scaling AI Agents/Assistants with GenAI frameworks. • Knowledge of observability and monitoring tools (LangSmith, Arize, GCP cloud logging). • Integration expertise with large enterprise tools (especially Salesforce). • Bonus: Terraform, front-end/UI skills (React/Node.js), or data engineering. Expectations • Sprint-based execution with measurable velocity. • Ramp-up and deliver value within the first 2–3 months. • Ability to translate complex business requirements into production-grade AI solutions. • Strong documentation, communication, and mentorship skills. • Curious, adaptable, and eager to drive innovation at scale. Why Join? • Work on 90+ AI use cases across multiple domains. • Be part of the first AI Pod with the opportunity to grow into multiple future pods. • Collaborate with top engineers while helping shape enterprise AI strategy. • Long-term project (12 months, with likely extension for years).