AI ML Engineer

• Python (Expert level)
• Machine Learning & Model Training
• Training, evaluation, fine tuning
• Tagging and labeling workflows
• Generative AI & LLMs
• Prompt engineering for LLM-based applications
• Document Processing
• Document extraction, parsing, and chunking
• Handling structured & unstructured data
• Embeddings & Vector Search
• Embedding generation
• Vector database integration
• Databases
• Vector Databases
• MongoDB
• Production-grade ML Engineering
• Scalable, production-ready ML/GenAI solutions

Roles & Responsibilities

This role is for a hands-on Data Science Engineer who will design, build, and deploy production‑grade Machine Learning and Generative AI solutions. The candidate must have strong Python expertise and practical experience taking ML and GenAI use cases from development to deployment.

The role focuses heavily on LLM-based applications, including prompt engineering, document processing pipelines, and embedding-based search solutions. The engineer will work with both structured and unstructured data, building pipelines for document extraction, parsing, and chunking, and integrating ML models with Vector Databases and MongoDB.

An ideal candidate is someone who understands end-to-end ML workflows—from data preparation, tagging, and labeling, through model training, evaluation, and fine-tuning—while ensuring solutions are scalable, high quality, and production ready.

For recruiter to interpret accurately

• Not a pure data analyst → this is an engineering-focused ML/GenAI role
• Not theoretical AI → requires real-world deployment experience
• Strong fit for candidates with backgrounds in:
• ML Engineering
• Applied Data Science
• GenAI / LLM application development