Lead AI/ ML Engineer with Google Cloud Platform and Senior AI/ ML Engineer with Google Cloud Platform - Remote role

Required Qualifications & Experience

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).
• Google Cloud Platform 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.

Domain & Architecture Focus

• 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).
• Data & Retrieval: Experience with vector indexing, hybrid search, reranking strategies, and chunking optimization for massive unstructured document stores.
• Security & HIPAA: Deep understanding of HIPAA compliance, PHI handling, and data privacy in AI pipelines.

Nice-to-Have / Force Multipliers

• Google Cloud Platform Cloud Architecture: Experience with Terraform, Google Cloud Platform VPCs, and IAM fundamentals.
• Fine-Tuning & Small Language Models (SLMs): Experience fine-tuning domain-specific models (PEFT, LoRA) for structured extraction or classification.
• Evaluation & Evals Frameworks: Deep experience with automated LLM benching and continuous integration testing for probabilistic software.

Certifications: Google Cloud Platform Professional Machine Learning Engineer or Google Cloud Platform Professional Cloud Architect credential