Lead AI ML Engineer

Role & responsibilities

• Expert-level Python for Production Systems: Proven ability to architect and write clean, typed, and testable code for distributed systems.
• Experience building systems that communicate via message queues like Azure Service Bus.
• In-depth knowledge of LLM vulnerabilities (OWASP Top 10 for LLMs) and experience implementing guardrails against prompt injections and data
• Asynchronous & Concurrent Programming: Mastery of Pythons asyncio to build scalable, I/O-bound services
• Deep, hands-on experience with FastAPI and Pydantic for building robust, schema-validated microservices
• Cloud-Native Development: Strong experience with Docker for applications and deploying orchestrated environments like Kubernetes (AKS).
• Responsible for designing, developing, and implementing machine learning models and algorithms to solve complex problems.

Preferred candidate profile

• LLM & RAG Architecture Expertise
• Hands-on experience with Retrieval-Augmented Generation (RAG) architectures using embeddings via Azure AI Search and Databricks.
• Familiar with MCP servers for scalable deployment.s.
• Familiar with MCP servers for scalable deployment.
• Agentic AI & Orchestration:
• Experience with autonomous decision support using LangGraph.
• Skilled in agent orchestration using Microsoft Copilot Studio and CrewAI.
• Performance Optimization:
• Working knowledge of latency reduction techniques for RAG-based applications, including Redis-based caching.
• LLM Fine-Tuning:
• Practical understanding of fine-tuning methods such as LoRA (Low-Rank Adaptation).
• Model Selection & Prompting:
• Awareness of the latest LLMs tailored to specific use cases (e.g., Claude, Gemini, GPT series).
• Understanding of prompt engineering requirements across different models.
• Cost Estimation:
• Ability to calculate and optimize costs for API-based model usage.

Domain Experience:

• Understanding of U.S. healthcare ecosystem, and regulatory frameworks (HIPAA, CMS), and reimbursement models.
• Hands-on expertise in working with clinical OR claims datasets, experience building and deploying ML solutions for prior authorization OR utilization management OR case/care management OR patient engagement OR Claims operations OR Provider operations OR population health analytics OR EHR systems translating domain nuances into feature engineering and evaluation strategies.
• Quality & outcomes orientation: Familiarity with U.S. quality frameworks (HEDIS, STAR ratings), value-based care initiatives and populationhealth objectives; ability to frame AI/ML work to drive measurable improvements in quality, affordability, and member/provider experience.