vinod.tiwari@tekgence.com
Job Role: Data Science Engineer
Job Location: Atlanta, Mason and Los Angels- local candidates only
Technical Skills:
- Advanced Python development for ML/AI workloads
- End‑to‑end ML lifecycle: model training, evaluation, fine‑tuning, and labeling/tagging workflows
- Generative AI systems design, including LLM-based application development
- Prompt engineering optimization for large language models
- Document AI pipelines: OCR/extraction, parsing, normalization, and text chunking for structured & unstructured data
- Embedding generation pipelines for semantic search and retrieval
- Vector similarity search implementation using vector databases
- ML model integration with Vector DBs and MongoDB
- Production‑grade ML engineering: scalable, maintainable, and deployment‑ready code
- Knowledge of CI/CD pipelines and cloud deployment (Azure preferred)
- Experience with Vector DBs and/or MongoDB
Python, Large Language Models (LLMs) (via LLM‑based applications), Vector Databases, MongoDB
Roles & Responsibilities
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 onstatistical modeling or academic research
- Lacking experience withdeployment, 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
Vinod Tiwari
Talent Acquisition Lead- US Recruitment