Description

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 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

Role Descriptions: Data Science Engineer (Customer Location Los Angels Mason)Role OverviewThe Data Science Engineer will develop scalable ML and Generative AI solutions| specializing in model training| document processing pipelines| and vector search implementations. Strong Python expertise and experience across modern ML and GenAI workflows are essential.Key Responsibilities- Develop and deploy Machine Learning and Generative AI solutions using Python- Design and refine prompt engineering strategies for LLM applications- Build document extraction| parsing| and chunking pipelines- Train| evaluate| and fine-tune ML models manage tagging and labeling workflows- Implement embedding generation and vector search solutions- Integrate ML models with vector databases and MongoDB- Ensure code quality| scalability| and production readinessRequired 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 andor MongoDB
Essential Skills: Data Science Engineer (Customer Location Los Angels Mason)Role OverviewThe Data Science Engineer will develop scalable ML and Generative AI solutions| specializing in model training| document processing pipelines| and vector search implementations. Strong Python expertise and experience across modern ML and GenAI workflows are essential.Key Responsibilities- Develop and deploy Machine Learning and Generative AI solutions using Python- Design and refine prompt engineering strategies for LLM applications- Build document extraction| parsing| and chunking pipelines- Train| evaluate| and fine-tune ML models manage tagging and labeling workflows- Implement embedding generation and vector search solutions- Integrate ML models with vector databases and MongoDB- Ensure code quality| scalability| and production readinessRequired 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 andor MongoDB
Desirable Skills:
Keyword:
Skills: Digital : Machine Learning~Digital : Mongo DB~Digital : Azure Machine Learning (ML)~Digital : Python for Data Science~AI & Gen AI - Products & Tools
Experience Required: 8-10

Key Skills

Education

Any Graduate