Data Science Practitioner

Data Science Practitioner

Project Role : Data Science Practitioner
Project Role Description : Formulating, design and deliver AI/ML-based decision-making frameworks and models for business outcomes. Measure and justify AI/ML based solution values.
Must have skills : Data Science
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education

AI Engineer / Data Scientist – NLP & Generative AI

Experience: 6–7 years
Location: India

Role Summary

Build and productionize NLP and generative AI capabilities — from classical NLP through LLM-based agentic workflows — for real-time, high-scale enterprise applications, with a focus on prompt engineering, RAG, and conversational AI experiences.

Key Responsibilities
Design, fine-tune, and optimize prompts for production LLM features (summarization, classification, sentiment, Q&A, translation, knowledge-gap analysis)
Build agentic workflows (e.g., CrewAI, LangChain, LangGraph, or equivalent) to orchestrate RAG pipelines and multi-step evaluation/validation tasks
Apply classical NLP techniques (clustering, sentiment analysis, NER, topic modeling, text classification) using transformer models alongside LLMs where appropriate
Develop and deploy real-time inference APIs to serve AI features to production applications at scale
Integrate speech-to-text and text-to-speech capabilities for conversational/simulation use cases
Build monitoring/analytics pipelines for productivity and quality metrics derived from AI outputs
Iterate on model performance through hyperparameter tuning, feedback loops, and evaluation against production data
Work across open-source and hosted LLMs, selecting the right model/deployment for cost, latency, and accuracy needs
Partner with data engineering on ingestion/synchronization pipelines across relational and vector data stores
Present KPI metrics, model performance, and insights to business stakeholders via dashboards

Required Skills & Experience

6+ years in NLP / data science / AI engineering with production deployment experience
Strong Python experience serving ML/AI models via production APIs
NLP fundamentals: text classification, NER, clustering, topic modeling, summarization, sentiment analysis
Transformer model experience: BERT/RoBERTa/T5/BART or equivalent
LLM and agentic tooling: prompt engineering, RAG, agentic frameworks (e.g., CrewAI, LangChain, LangGraph), and LLM APIs (e.g., GPT, Claude, or open-source models)
Classical ML: supervised/unsupervised algorithms and evaluation methodology
Hands-on depth with Azure AI and compute services — Azure AI Foundry, Azure Function Apps, Azure Logic Apps — or equivalent AWS/GCP AI and serverless stacks
SQL databases plus exposure to a vector store
Preferred
Knowledge graph / graph-based retrieval techniques for RAG (e.g., Neo4j or equivalent)
Production deployment experience across multiple major cloud platforms (AWS, Azure, and GCP) — spanning AI/ML services, managed databases, and serverless compute
Speech-to-text/text-to-speech integration experience for conversational AI use cases
Experience with no-code/low-code platforms (e.g., Microsoft Power Platform, Copilot Studio, or equivalent) for rapid agent/bot prototyping
Dashboarding (Power BI/Tableau)
Exposure to healthcare or other regulated-data domains

Education
Bachelor's/master's in data science, Computer Science, Statistics, or related field