Data scientist

Key Responsibilities:
Agent Development & Architecture:
Build agentic workflows using Lang Chain/Lang Graph and similar frameworks. • Develop autonomous agents for data validation, reporting, document processing, and domain workflows
Deploy scalable, resilient agent pipelines with monitoring and evaluation
Gen AI Application Engineering
Develop Gen AI applications using models like GPT, Gemini, and LLa M
Implement RAG, vector search, prompt orchestration, and model evaluation
Partner with data scientists to productionize POCs
Data & Platform Engineering
Build distributed data pipelines (Python, Py Spark)
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 Gen AI 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 Gen AI capability building.
Key Requirements:
8-12 years of experience in AI/ML engineering, data science, or software engineering, with atleast 2 years focused on Gen AI
Strong programming expertise in Python, distributed computing using Py Spark, and API development
Hands on experience with LLM frameworks (Lang Chain, Lang Graph, Transformers, Open AI/Vertex/Bedrock SDKs)
Experience developing AI agents, retrieval pipelines, tool calling structures, or autonomous task orchestration
Solid understanding of Gen AI 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