Description

You will design and lead the architectural vision for agentic AI solutions, integrating models, orchestration, memory, and tool use across healthcare and enterprise platforms.

This role is on-site.

Responsibilities

• Define end-to-end architectures for agentic AI systems, ensuring scalability and reliability.
• Design and deploy multi-agent AI systems on cloud platforms using MLOps best practices.
• Lead integration of agentic AI solutions with existing healthcare and enterprise platforms, ensuring data interoperability.
• Guide and mentor teams on architectural patterns for scalable, reliable agentic AI systems.
• Ensure AI governance, security, and ethical practices are implemented throughout the agent lifecycle, adhering to regulations.

Required Skills

• 10+ years of experience in software architecture or engineering, including 5+ years focused on AI/ML.
• Proven experience designing and developing multi-agent AI systems in production.
• Deep expertise in multi-agent orchestration frameworks: LangChain, LangGraph, CrewAI, AutoGen.
• Strong knowledge of LLM architectures, RAG implementation, and model fine-tuning techniques.
• Extensive experience with cloud platforms (AWS, Azure, or GCP) and their AI services.
• Proficiency in Python and relevant AI/ML libraries (PyTorch).
• Hands-on experience with MLOps practices using Docker, Kubernetes, and MLflow.
• Significant experience in the healthcare industry, understanding HL7, FHIR, and clinical workflows.

Key Skills

Education

Any Graduate