Data Scientist

Dun & Bradstreet India · India

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