Lead AI/ML Engineer

Key Skills:

-         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 Python’s 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.

Must have skills

• LLM & RAG Architecture Expertise
• Hands-on experience with Retrieval-Augmented Generation (RAG) architectures using embeddings via Azure AI Search and Databricks.
• Proficient in implementing semantic search capabilities.
• 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.

Functional/Team experience