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: 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

Key Skills

Education

Any Graduate