Description

 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).

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.

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: E

xperience 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 GCP Cloud Architecture:

Experience with Terraform, GCP 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: GCP Professional Machine Learning Engineer or GCP Professional Cloud Architect credential

Key Skills

Education

Any Gradute