AI/ML Architect -Solutions Architect

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
• 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
Design real-time inference services integrated with Epic via FHIR APIs
Ensure HIPAA-compliant encryption, access controls, and audit trails