Data Scientist

Dun & Bradstreet; Chennai District; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Dun & Bradstreet in Chennai, India, seeks an experienced AI Engineer to design and operationalize AI-driven solutions for its global Analytics organization.

The ideal candidate has hands-on Python, PySpark, GenAI frameworks, and experience building production-grade systems with LLMs, retrieval pipelines, and automation.

You will collaborate with data scientists, MLOps, and stakeholders to deliver scalable AI applications across cloud and hybrid environments.

Qualifications

• 5-8 years in AI/ML engineering, data science, or software engineering
• Strong Python and PySpark coding experience
• Hands-on with GenAI frameworks and LLMs
• Experience building AI agents, retrieval pipelines, or orchestration
• Knowledge of prompting, embeddings, RAG and evaluation metrics
• Cloud experience (Azure/AWS/GCP) and CI/CD for ML/AI
• Excellent communication and problem-solving skills

Responsibilities

• Design and build agentic workflows using LangChain/LangGraph
• Develop autonomous agents for data validation, reporting, and document processing
• Deploy scalable agent pipelines with monitoring and evaluation
• Develop GenAI applications using GPT, Gemini and LLaMA
• Implement RAG, vector search, prompt orchestration and model evaluation
• Partner with data scientists to productionize POCs
• Collaborate with analytics, product and engineering teams

Skills

Python

PySpark

LangChain

LangGraph

GenAI frameworks

LLMs

Retrieval pipelines

Automation pipelines

APIs

Cloud platforms

Docker

CI/CD

MLOps

Tools

LangChain

LangGraph

Transformers

OpenAI SDK

Vertex SDK

Bedrock SDK

Docker

Job description

The Role

We are looking for an experienced AI Engineer to design, build, and operationalize AI driven solutions for our global Analytics organization.

The ideal candidate will have strong hands on expertise in Python, PySpark, agentic workflow development, and modern GenAI frameworks, with experience building scalable applications using LLMs, retrieval systems, and automation pipelines.

You will work closely with data scientists, MLOps engineers, and business stakeholders to build intelligent, production grade systems that power our analytics capabilities.

Key Responsibilities

1. 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.
2. GenAI Application Engineering
   • Develop GenAI applications using models like GPT, Gemini, and LLaMA.
   • Implement RAG, vector search, prompt orchestration, and model evaluation.
   • Partner with data scientists to productionize POCs.
3. 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.
4. MLOps / LLMOps
   • Contribute to CI/CD workflows for AI models—deployment, testing, monitoring.
   • Implement governance, guardrails, and reusable GenAI frameworks.
5. 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 Skills & Requirements

• 5-8 years of experience in AI/ML engineering, data science, or software engineering, with at least 4 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.