Description

Build and deploy scalable machine learning and generative AI solutions, focusing on LLM applications, document AI pipelines, and vector search systems.

This role is hybrid.

Responsibilities

• Develop and deploy ML and GenAI solutions using expert-level Python.
• Design and optimize prompt engineering strategies for LLM-based applications.
• Build document extraction, parsing, and chunking pipelines for structured and unstructured data.
• Implement embedding generation and vector similarity search using vector databases and MongoDB.
• Train, evaluate, and fine-tune ML models while managing tagging and labeling workflows.

Required Skills

• Expert-level proficiency in Python for ML/AI workloads.
• 8+ years of experience in data science or machine learning engineering.
• Hands-on experience with Large Language Models (LLMs) and generative AI systems.
• Strong knowledge of vector databases and MongoDB integration.
• Experience with end-to-end ML lifecycle: model training, evaluation, and deployment.
• Proficiency in document AI pipelines including OCR, extraction, and text chunking.
• Understanding of CI/CD pipelines and cloud deployment (Azure preferred).

Preferred Skills

• Experience with semantic search and retrieval-augmented generation (RAG) architectures.

Key Skills

Education

Any Graduate