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Burbank, CA, USA
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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: Role OverviewWe 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 MLGenAI frameworks.Key ResponsibilitiesDevelop and deploy machine learning and GenAI solutions using PythonDesign and optimize prompt engineering strategies for LLM-based applicationsBuild document extraction| parsing| and chunking pipelines for structured and unstructured dataTrain| evaluate| and fine-tune ML models manage tagging and labeling workflowsImplement embedding generation and vector search solutionsIntegrate ML models with Vector DBs and MongoDBEnsure code quality| scalability| and production readinessRequired QualificationsExpert-level proficiency in PythonStrong experience in model training| evaluation| and tagging workflowsHands-on experience with document extraction and chunking techniquesSolid understanding of ML algorithms and Generative AI conceptsExperience working with Vector Databases andor MongoDB
Essential Skills: Role OverviewWe 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 MLGenAI frameworks.Key ResponsibilitiesDevelop and deploy machine learning and GenAI solutions using PythonDesign and optimize prompt engineering strategies for LLM-based applicationsBuild document extraction| parsing| and chunking pipelines for structured and unstructured dataTrain| evaluate| and fine-tune ML models manage tagging and labeling workflowsImplement embedding generation and vector search solutionsIntegrate ML models with Vector DBs and MongoDBEnsure code quality| scalability| and production readinessRequired QualificationsExpert-level proficiency in PythonStrong experience in model training| evaluation| and tagging workflowsHands-on experience with document extraction and chunking techniquesSolid understanding of ML algorithms and Generative AI conceptsExperience working with Vector Databases andor MongoDB
Desirable Skills:
Keyword:
Skills: Digital : Amazon Web Service(AWS) Cloud Computing~Digital : Machine Learning~Digital : Mongo DB~Digital : Python for Data Science~Generative AI~AI & Gen AI - Products & Tools
Experience Required: 10 & Above
Any Graduate
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