Responsibilities

• Define the architectural vision for agentic AI solutions, including model integration, orchestration frameworks (e.g., LangChain, LangGraph), memory systems, and tool-use capabilities.
• Lead and mentor teams of AI engineers, data scientists, and DevOps specialists, guiding them on architectural patterns, best practices, and scalable system design.
• Design, deploy, and manage multi-agent AI systems on cloud platforms (AWS, Azure, or GCP), ensuring AI pipelines are optimized with MLOps practices for monitoring, scaling, and evaluation.
• Lead integration efforts of AI solutions into healthcare systems, ensuring data interoperability, security, and compliance with regulations (e.g., HIPAA, HL7, FHIR).
• Implement AI governance frameworks, ensuring fairness, transparency, and compliance with healthcare regulations. Lead efforts in bias mitigation and ethical AI practices.
• Oversee the development of PoC initiatives, validate new AI capabilities, and scale successful prototypes into production-ready systems.
• Assess and integrate AI tools, including vector databases and orchestration frameworks, to build cutting-edge AI solutions.
• Stay current on the latest trends in agentic AI and multi-agent systems. Contribute to the AI community by driving innovation and presenting at industry conferences.

Required Qualifications

• Experience: 10+ years in software architecture or engineering, with 5+ years specializing in AI/ML. Proven experience developing multi-agent AI systems in production.
• Healthcare Industry Expertise: Significant experience in the healthcare sector, understanding clinical workflows, RCM, and healthcare data standards (e.g., HL7, FHIR).
• Technical Skills:
• Expertise in multi-agent orchestration frameworks (e.g., LangChain, LangGraph, CrewAI).
• Deep knowledge of LLM architectures, RAG implementation, and fine-tuning models.
• Extensive experience with cloud platforms (AWS, Azure, GCP) and AI services.
• Strong background in data engineering, ETL pipelines, and vector store management.
• Proficiency in Python and AI/ML libraries (e.g., PyTorch, TensorFlow).
• Hands-on experience with MLOps tools (e.g., Docker, Kubernetes, MLflow).
• AI Governance: Strong understanding of AI governance, ethics, and compliance, particularly in regulated environments like healthcare.

Preferred Qualifications

• Education: Advanced degree (Master’s or PhD) in Computer Science, AI, Data Science, or a related field.
• Certifications: Relevant certifications in cloud platforms (AWS, Azure, GCP) or AI/ML.
• Thought Leadership: Experience presenting at conferences, contributing to research, or writing thought leadership articles on AI/ML topics.

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