Job Description
Must Have Technical/Functional Skills
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• Experience:
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o Must have SI experience with larger IT service provider
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o 10+ years of experience in software architecture or engineering, with at least 5+ years in AI/ML specifically.
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o Proven experience designing and developing multi-agent AI systems in a production environment.
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o Significant experience in the healthcare industry, with a deep understanding of clinical workflows, RCM,
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data standards (HL7, FHIR), and regulated environments.
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• Technical skills:
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o Expertise in multi-agent orchestration frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen).
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o Deep knowledge of LLM architectures, RAG implementation, and techniques for fine-tuning models.
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o Extensive experience with cloud platforms (AWS, Azure, or GCP) and related AI services.
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o Strong background in data engineering, including building ETL pipelines and managing vector stores.
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o Proficiency in Python and relevant AI/ML libraries (e.g., PyTorch, TensorFlow).
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o Hands-on experience with MLOps practices and tools (e.g., Docker, Kubernetes, MLflow).
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Roles & Responsibilities
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• System architecture: Define the architectural vision and strategy for agentic AI solutions, designing end-to-end architectures
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that include model integration, orchestration frameworks, memory systems, and tool-use capabilities.
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• Technical leadership: Guide and mentor cross-functional teams of AI engineers, data scientists, and DevOps specialists on
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architectural patterns and best practices for building scalable and reliable agentic AI systems.
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• Cloud infrastructure and MLOps: Design and deploy multi-agent AI systems on cloud platforms (AWS, Azure, or GCP),
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building and managing cloud-native AI pipelines with MLOps best practices for monitoring, evaluating, and scaling agents.
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• Healthcare integration: Lead the integration of agentic AI solutions with existing healthcare systems, and other enterprise platforms,
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while ensuring data interoperability and security.
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• Responsible AI: Ensure the implementation of strong AI governance, security, and ethical practices throughout the agent lifecycle,
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including bias mitigation, fairness checks, and compliance with healthcare regulations like HIPAA.
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• Proof of concept and scaling: Lead proof-of-concept (PoC) initiatives to validate new agentic capabilities, then develop
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strategies to scale successful prototypes into production-ready systems.
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• Technology evaluation: Evaluate and integrate a wide range of open-source and proprietary AI tools and technologies,
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including vector databases, orchestration frameworks (e.g., LangChain, CrewAI), and cloud-native AI services.
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• Thought leadership: Stay current with the latest advancements in agentic AI, generative models, and multi-agent frameworks,
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driving innovation within the company and potentially presenting at industry conferences.
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