AI Architect

AI Architect

Location:

New York, NY / Edison, NJ / Chicago, IL

FTE Job Description

• Must have SI experience with larger IT service provider
• 10+ years of experience in software architecture or engineering, with at least 5+ years in AI/ML specifically.
• Proven experience designing and developing multi-agent AI systems in a production environment.
• Significant experience in the healthcare industry, with a deep understanding of clinical workflows, RCM, data standards (HL7, FHIR), and regulated environments.

• Expertise in multi-agent orchestration frameworks (e.g., Lang Chain, Lang Graph, CrewAI, Auto Gen).
• Deep knowledge of LLM architectures, RAG implementation, and techniques for fine-tuning models.
• Extensive experience with cloud platforms (AWS, Azure, or GCP) and related AI services.
• Strong background in data engineering, including building ETL pipelines and managing vector stores.
• Proficiency in Python and relevant AI/ML libraries (e.g., PyTorch, Tensor Flow).
• Hands-on experience with MLOps practices and tools (e.g., Docker, Kubernetes, MLflow).

• System architecture:
  Define the architectural vision and strategy for agentic AI solutions, designing end-to-end architectures that include model integration, orchestration frameworks, memory systems, and tool-use capabilities.
• Technical leadership:
  Guide and mentor cross-functional teams of AI engineers, data scientists, and Dev Ops specialists on architectural patterns and best practices for building scalable and reliable agentic AI systems.
• Cloud infrastructure and MLOps:
  Design and deploy multi-agent AI systems on cloud platforms (AWS, Azure, or GCP), building and managing cloud-native AI pipelines with MLOps best practices for monitoring, evaluating, and scaling agents.
• Healthcare integration:
  Lead the integration of agentic AI solutions with existing healthcare systems, and other enterprise platforms, while ensuring data interoperability and security.
• Responsible AI:
  Ensure the implementation of strong AI governance, security, and ethical practices throughout the agent lifecycle, including bias mitigation, fairness checks, and compliance with healthcare regulations like HIPAA.
• Proof of concept and scaling:
  Lead proof-of-concept (PoC) initiatives to validate new agentic capabilities, then develop strategies to scale successful prototypes into production-ready systems.
• Technology evaluation:
  Evaluate and integrate a wide range of open-source and proprietary AI tools and technologies, including vector databases, orchestration frameworks (e.g., Lang Chain, CrewAI), and cloud-native AI services.
• Thought leadership:
  Stay current with the latest advancements in agentic AI, generative models, and multi-agent frameworks, driving innovation within the company and potentially presenting at industry conferences.