We are seeking a highly skilled Data Science Engineer to design and develop scalable ML and Generative AI solutions. The ideal candidate will have deep expertise in Python, hands-on experience in model training, document processing pipelines, and strong knowledge of vector databases and modern ML/GenAI frameworks.
Strong fit if the candidate:
Has expert-level Python skills
Has hands-on experience building ML/GenAI systems, not just theoretical knowledge
Has worked on end-to-end ML pipelines (data → model → deployment)
Has experience with document AI, embeddings, and vector search
Thinks like an engineer (scalable, maintainable, production-ready code)
Likely not a fit if the candidate is:
Primarily a BI / reporting analyst
Focused only on statistical modeling or academic research
Lacking experience with deployment, pipelines, or GenAI systems
Key Responsibilities:
Develop and deploy machine learning and GenAI solutions using Python
Design and optimize prompt engineering strategies for LLM-based applications
Build document extraction, parsing, and chunking pipelines for structured and unstructured data
Train, evaluate, and fine-tune ML models; manage tagging and labeling workflows
Implement embedding generation and vector search solutions
Integrate ML models with Vector DBs and MongoDB
Ensure code quality, scalability, and production readiness
Required Qualifications:
Expert-level proficiency in Python
Strong experience with model training, evaluation, and tagging workflows
Hands-on experience with document extraction and chunking techniques
Solid understanding of ML algorithms and Generative AI concepts
Experience with vector databases and/or MongoDB