AI/LLM Technical Lead
On-siteHaveli, Uttar Pradesh, Republic of India or Haveli, State of Mahārāshtra, Republic of India
Job Summary
Lead architecture, design, and implementation of LLM-based and agentic AI systems for clinical and operational use cases, overseeing multi-agent orchestration frameworks and scalable RAG pipelines. Guide engineers on prompt design, model evaluation, and hallucination control while managing the end-to-end AI lifecycle from data ingestion to deployment on Vertex AI or AWS Bedrock. Collaborate with product managers, data engineers, and designers to align AI architecture with business goals, ensuring compliance with HIPAA, PHI safety, and responsible AI governance. Lead scrum ceremonies, sprint planning, and backlog prioritization for the AI team, working directly with external stakeholders to translate insights into scalable solutions. Mentor and upskill the engineering team while shaping the AI roadmap in an innovation-first culture.
Required Qualifications
- Deep expertise in LLMs, RAG, and Agentic AI architectures, including multi-agent planning and task orchestration
- Hands-on experience with LangChain, LangGraph, CrewAI, or Semantic Kernel
- Strong proficiency in Python, cloud-native systems, and microservice-based deployments
- Proven track record of leading AI projects from concept to production, including performance optimization and monitoring
- Experience working with healthcare data models (FHIR, HL7, clinical notes) or similar regulated domains
- Experience leading agile/scrum teams, with strong sprint planning and delivery discipline
- Excellent communication and collaboration skills for customer-facing discussions, technical presentations, and cross-team coordination
- Deep understanding of prompt engineering, LLM evaluation, and hallucination mitigation
- Strong leadership, mentorship, and people management abilities
- Excellent written and verbal communication for both technical and non-technical audiences
- Ability to balance technical depth with product priorities and delivery timelines
- Adaptability to fast-changing AI technologies and ability to evaluate new tools pragmatically
- A bias toward ownership and proactive problem-solving in ambiguous situations
- Empathy for end-users and a commitment to responsible AI in healthcare
Desired Qualifications
- Experience leading AI platform initiatives or building internal AI tooling
- Exposure to MLOps, continuous evaluation pipelines, and observability tools for LLM systems
- Knowledge of multi-modal AI (text + structured + image data)
- Prior experience integrating AI into production SaaS platforms or healthcare systems
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