AI/LLM Technical Lead

Posted 15 Hours Ago
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Haveli, Pune, Maharashtra, IND
In-Office
Entry level
Artificial Intelligence • Healthtech • Software
The Role
Lead the architecture, development, and deployment of LLM-based and agentic AI systems for healthcare and operational applications. Oversee multi-agent orchestration, RAG pipelines, prompt engineering, evaluation, monitoring, and hallucination mitigation. Collaborate with product, data, design, customers, and clinical stakeholders; lead Scrum delivery, mentor engineers, support hiring, and ensure HIPAA, PHI safety, and responsible AI compliance.
Summary Generated by Built In

Responsibilities

  • Lead architecture, design, and implementation of LLM-based and agentic AI systems for clinical and operational use cases.
  • Oversee the development of multi-agent orchestration frameworks (reasoning, planning, and task execution) using tools such as LangGraph, CrewAI, or Semantic Kernel.
  • Build scalable RAG pipelines and retrieval systems using vector databases (Pinecone, FAISS, Weaviate, Vertex AI Matching Engine).
  • Guide engineers on prompt design, model evaluation, multi-step orchestration, and hallucination control.
  • Collaborate with product managers, data engineers, and designers to align AI architecture with business goals.
  • Manage end-to-end AI lifecycle — data ingestion, fine-tuning, evaluation, deployment, and monitoring on Vertex AI / AWS Bedrock / Azure OpenAI.
  • Lead scrum ceremonies, sprint planning, and backlog prioritization for the AI team.
  • Work directly with external stakeholders and customer teams to understand requirements, gather feedback, and translate insights into scalable AI solutions.
  • Ensure compliance with HIPAA, PHI safety, and responsible AI governance practices.
  • Contribute to hiring, mentoring, and upskilling the AI engineering team.


Requirements

Must-Have Skills

  • 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.


General Skills

  • 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.


Good to Have

  • 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.


Benefits

Why Join Fold Health

  • Lead the development of next-generation AI systems transforming healthcare delivery.
  • Collaborate with world-class data, product, and clinical teams on meaningful challenges.
  • Shape the AI roadmap and mentor a growing team of engineers in an innovation-first culture.
  • Work on cutting-edge Agentic AI and LLM applications deployed in real-world healthcare settings.


Skills Required

  • 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
  • Experience leading AI projects from concept through production, including performance optimization and monitoring
  • Experience working with healthcare data models such as FHIR, HL7, and clinical notes, or similar regulated domains
  • Experience leading agile or Scrum teams, including sprint planning and delivery
  • Excellent communication and collaboration skills for customer 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 technical and non-technical audiences
  • Ability to balance technical depth with product priorities and delivery timelines
  • Adaptability to rapidly changing AI technologies and ability to evaluate tools pragmatically
  • Ownership and proactive problem-solving in ambiguous situations
  • Empathy for end users and commitment to responsible AI in healthcare
  • Experience leading AI platform initiatives or building internal AI tooling
  • Exposure to MLOps, continuous evaluation pipelines, and observability tools for LLM systems
  • Knowledge of multimodal AI involving text, structured data, and image data
  • Experience integrating AI into production SaaS platforms or healthcare systems
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The Company

What We Do

Fold Health develops and scales a healthcare technology platform for clinical and operational use cases. The company combines software engineering, healthcare data standards, cloud-native infrastructure, and AI/ML technologies to support healthcare workflows. Its engineering organization builds scalable solutions and explores LLM-based and agentic AI systems, while its product focus includes value-based care and healthcare data integration.

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