AI/ML Architect (AI Solutions Architect)

Role Summary:

Design the architecture for deploying scalable, observable, and compliant AI/ML solutions integrated with clinical workflows and hospital data platforms.

Responsibilities:

• Architect end-to-end ML pipelines using Airflow, and MLFlow or Vertex AI

• Build containerized model inference systems deployed on Kubernetes with AppDynamics and observability

• Full ownership of model lifecycle: development, deployment, drift monitoring

• Proficient with Google Cloud Platform Vertex AI, Azure ML, container orchestration (K8s, Docker)

• Design architecture to support GenAI workloads (LLMs, embeddings) using Vertex AI or Azure OpenAI

• Define governance and guardrails for deploying agentic systems in clinical workflows

• Design real-time inference services integrated with Epic via FHIR APIs

• Ensure HIPAA-compliant encryption, access controls, and audit trails

• Implement MLOps patterns: model versioning, rollback, shadow testing

• Define architecture for RAG (retrieval augmented generation) systems using vector databases (e.g., FAISS, Pinecone)

• Deploy LLM-based agents and secure GenAI pipelines (prompt injection protection, moderation, output fallback)

• Support agentic AI orchestration with frameworks like LangChain, CrewAI

Required Qualifications:

• 8+ years in data/ML or AI architecture roles

• Deep knowledge of Kubernetes, Docker, Snowflake, cloud-native tools (Google Cloud Platform, Azure)

• Experience with HIPAA-regulated, real-time model deployment

Preferred Qualifications:

• Experience integrating with Epic, HL7, FHIR, and SMART-on-FHIR

• Working knowledge of LLMs, GenAI tools, LangChain, Weaviate, or ChromaDB