Job Description

Key Responsibilities

1. AI/ML Model Development

Build, fine-tune, and evaluate ML/LLM models for classification, prediction, summarization, RAG, and agentic workflows. Develop modular, reusable model pipelines supporting multi-tenant architectures. Conduct experimentation, A/B testing, and performance tuning.

2. AI Agent & Orchestration Development

Implement agentic frameworks (multi-step reasoning, workflows, tool use). Integrate AI agents into orchestration layers (Drools, microservices, API gateways). Build domain-specific ontologies, embeddings, vector search, and retrieval layers.

3. Data Engineering & Feature Pipelines

Build ETL/ELT pipelines, feature stores, and real-time data ingestion using Python and SQL. Work with structured/unstructured data including EDI, FHIR, PDFs, medical records.

4. Platform & API Integration

Develop secure, scalable APIs (REST/GraphQL) for embedding AI into product workflows. Integrate AI modules with microservices, Postgres DB, event queues, and messaging layers. Work closely with DevOps to deploy models into containerized environments (AWS, Docker, Lambda).

5. Enterprise-Grade AI Delivery

Implement monitoring, guardrails, safety filters, hallucination-mitigation and auditability. Ensure HIPAA, PHI, SOC2, CMS-compliant AI operations. Drive MLOps best practices including CI/CD pipelines, versioning, and model governance.

6. Cross-Functional Collaboration

Work with product, clinical, engineering, and client teams to translate real-world use cases into scalable AI solutions. Collaborate with SMEs in UM/CM, provider operations, risk, fraud, and claims workflows.

Required Skills & Experience

Technical Skills

Strong Python development (FastAPI, LangChain, LlamaIndex, PyTorch, transformers). Hands-on LLM experience (OpenAI, AWS Bedrock, local models like Llama, Mistral). Experience with vector databases (Pinecone, PGVector, Chroma). Solid SQL experience (PostgreSQL preferred). Experience building microservices and event-driven systems. Knowledge of AWS stack: Lambda, S3, EC2, API Gateway, Step Functions. Experience with AI agents, RAG pipelines, and orchestration frameworks. Familiarity with healthcare standards (FHIR, EDI 278/837/835) is a strong plus.

Soft Skills

Strong problem-solving, analytical thinking, and debugging skills. Ability to work in fast-paced, ambiguous, product-driven environments. Excellent communication skills for cross-functional collaboration.

Preferred Qualifications

12-18+ years of experience in AI/ML engineering or software engineering with AI integration. Experience in payers, providers, health tech, or regulated industries. Exposure to rule engines (Drools/Blaze/Sparkling Logic) and BPM/orchestration systems. Experience with RPA, document processing, OCR/NLP, or medical record extraction.

Qualifications

Graduate

Range of Year Experience-Min Year

12

Range of Year Experience-Max Year

18