Data Scientist

Key Responsibilities

Agent Development & Architecture

Build agentic workflows using LangChain/LangGraph and similar frameworks. • Develop autonomous agents for data validation, reporting, document processing, and domain workflows

Deploy scalable, resilient agent pipelines with monitoring and evaluation

GenAI Application Engineering

Develop GenAI applications using models like GPT, Gemini, and LLaM

Implement RAG, vector search, prompt orchestration, and model evaluation

Partner with data scientists to productionize POCs

Data & Platform Engineering

Build distributed data pipelines (Python, PySpark)

Develop APIs, SDKs, and integration layers for AI-powered applications

Optimize systems for performance and scalability across cloud/hybrid environments.

MLOps / LLMOps

Contribute to CI/CD workflows for AI models-deployment, testing, monitoring

Implement governance, guardrails, and reusable GenAI frameworks.

Collaboration & Stakeholder Engagement

Work with analytics, product, and engineering teams to define and deliver AI solutions

Participate in architecture reviews and iterative development cycles

Support knowledge sharing and internal GenAI capability building.

Key Requirements

8-12 years of experience in AI/ML engineering, data science, or software engineering, with atleast 2 years focused on GenAI

Strong programming expertise in Python, distributed computing using PySpark, and API development

Hands on experience with LLM frameworks (LangChain, LangGraph, Transformers, OpenAI/Vertex/Bedrock SDKs)

Experience developing AI agents, retrieval pipelines, tool calling structures, or autonomous task orchestration

Solid understanding of GenAI concepts: prompting, embeddings, RAG, evaluation metrics, hallucination identification, model selection, fine tuning, context engineering

Experience with cloud platforms (Azure/AWS/GCP), containerization (Docker), and CI/CD pipelines for ML/AI

Strong problem solving, system design thinking, and ability to translate business needs into scalable AI solutions

Excellent verbal, written communication and presentation skills. Good to Have

Experience in workflow automation and building reusable AI components

Background in analytics, statistical models, or enterprise data products

Experience with MLOps / LLMOps tooling