AI/ML GCP Engineer

Healthcare Payer Agentic Platform Architecture: Design and implement end-to-end multi-agent orchestration systems (using LangGraph, AutoGen, CrewAI, or custom DAG runners). Forward Deployed Engineering & Client Value Delivery: Embed directly with engineering, product, and client business stakeholders to translate messy healthcare operational processes (e.g., prior auth rules, claims denial handling) into deterministic agent tools and graphs. Production AI/ML Engineering on GCP: Build, deploy, and maintain high-throughput, low-latency microservices using Python, Docker, Cloud Run, and GKE. Healthcare Data, Compliance & Security: Engineer AI agent tools that interface seamlessly with enterprise Payer data standards, including HIPAA-compliant environments, FHIR/HL7 APIs, and EDI transactions (837, 835, 270/271, 278). Observability, Guardrails & Responsible AI: Deploy comprehensive telemetry to track agent trajectory, tool calling success rate, token usage, and sub-second reasoning latency using Google Cloud Operations.

Core AI/ML & Agentic Engineering (Must-Have): 8+ years in software/AI engineering, with 3+ years directly building, deploying, and maintaining production-grade LLM applications, RAG pipelines, or autonomous agent frameworks. Agentic Frameworks: Hands-on mastery of multi-agent orchestration patterns, tool calling, stateful graphs, and memory management (e.g., LangGraph, AutoGen, LlamaIndex, Semantic Kernel). GCP AI Ecosystem: Deep experience with Vertex AI (Model Garden, Endpoint Deployment, Vector Search, Workbench) and cloud-native services (Cloud Run, Pub/Sub, Cloud Functions). Production Python Engineering: Advanced Python expertise (AsyncIO, FastAPI, Pydantic, gRPC) writing clean, tested, and containerized microservices. Healthcare / Payer Domain: Proven familiarity with Payer workflows (Prior Authorization, Claims Processing, Appeals, Member Engagement) and health data standards (FHIR, EDI X12, ICD-10/CPT).