Onsite : Lead ML Engineer II – RAG / Gen AI Platform Architect (Open AI, MongoDB Atlas, AWS

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Job Summary

We are seeking a highly experienced Lead ML Engineer / Lead RAG Engineer to architect and deliver a production-grade Retrieval-Augmented Generation (RAG) platform. The ideal candidate will have deep expertise in Python, OpenAI APIs, MongoDB Atlas Vector Search, AWS data platforms, and enterprise-scale AI solution delivery.

This role will lead the design and implementation of end-to-end RAG architecture, including ingestion, indexing, retrieval, grounding, citations, tool integrations, observability, and production readiness.

Required Experience

• 10+ years of hands-on software engineering and Python development
• Proven experience leading production-grade RAG/LLM platforms
• Strong expertise with OpenAI APIs (Embeddings, Chat Completions, Tool Calling)
• Hands-on experience with MongoDB Atlas Vector Search
• Experience integrating AWS Aurora MySQL and AWS DocumentDB
• Strong API development experience using FastAPI
• Experience with asynchronous processing, data pipelines, batch jobs, and event-driven architectures
• Expertise in CI/CD, testing frameworks, Docker, monitoring, and security best practices
• Experience mentoring engineers and leading architecture reviews

Key Responsibilities

• Own end-to-end RAG architecture from ingestion through generation
• Design scalable extraction pipelines from Aurora MySQL and DocumentDB
• Build chunking, metadata, embedding, and indexing strategies
• Implement vector search, metadata filtering, and multi-tenant retrieval
• Integrate OpenAI models for grounded response generation and citations
• Establish MCP-style tool integrations with auditability and governance
• Drive production readiness, observability, security, and reliability
• Lead technical design reviews, coding standards, and mentoring initiatives

Must-Have Skills

• End-to-end RAG Architecture & LLM Orchestration – 10+ Years
• Python Backend/Data Engineering – 8+ Years
• OpenAI APIs (Embeddings, Chat, Tools) – 6+ Years
• MongoDB Atlas Vector Search – 6+ Years
• AWS Aurora MySQL & DocumentDB Integration – 6+ Years
• FastAPI, Async Pipelines, CI/CD – 6+ Years

Nice to Have

• Hybrid Retrieval (Keyword + Vector)
• Reranking frameworks
• RAGAS, TruLens, LLM Evaluation Frameworks
• OCR, Document Parsing, Knowledge Graphs
• AWS ECS/EKS/Lambda deployments
• Healthcare domain experience

Preferred Technologies

• Python, FastAPI
• OpenAI APIs
• MongoDB Atlas Vector Search
• AWS Aurora MySQL
• AWS DocumentDB
• Docker, Kubernetes, ECS/EKS
• Kafka, SQS, Celery, RQ
• Pytest, CI/CD Pipelines
• Monitoring & Observability Stack

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Thanks,

Everest Global Solutions INC

Email  :Jo...@everestglobalsolutionsinc.com