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At Dun & Bradstreet, we believe data can drive meaningful change. As a global leader in business decisioning data and analytics, we empower companies around the world to grow, manage risk, and innovate. For more than 180 years, organizations have relied on us to transform uncertainty into opportunity. Our diverse, international team values creativity, collaboration, and bold thinking. Ready to make an impact and help shape the future? Join us and explore opportunities at dnb.com/careers.

We’re seeking a highly capable AI Tool/Agent Testing Engineer to assess, validate, and ensure the reliability of AI agents, automation tools, and agentic workflows used throughout our analytics platform. This position combines test engineering, GenAI expertise, Python and PySpark skills, and knowledge of the agent development lifecycle. You will collaborate closely with Data Science, AI Engineering, and Platform teams to ensure AI agents operate safely, consistently, and in compliance with business and regulatory standards.

Key Responsibilities:
• Build agentic workflows using LangChain, LangGraph, and related frameworks
• Create autonomous agents for tasks such as data validation, reporting, document processing, and domain-specific workflows
• Deploy scalable and resilient agent pipelines with monitoring and evaluation
• Develop GenAI applications using models like GPT, Gemini, and LLaMA
• Implement RAG, vector search, prompt orchestration, and model evaluation methods
• Collaborate with data scientists to move POCs into production
• Build distributed data pipelines using Python and PySpark
• Develop APIs, SDKs, and integration components for AI-driven applications
• Optimize systems for performance and scalability in cloud or hybrid environments
• Contribute to CI/CD pipelines for AI models, including deployment, testing, and monitoring
• Establish governance, guardrails, and reusable GenAI frameworks
• Work with analytics, product, and engineering teams to design and deliver AI solutions
• Participate in architecture discussions and iterative development processes
• Support internal knowledge sharing and strengthen GenAI expertise across teams

Key Skills:
• 6–8 years of experience in AI/ML engineering, data science, or software engineering, including at least 2 years in GenAI
• Strong programming skills in Python, distributed computing with PySpark, and API development
• Practical experience with LLM frameworks such as LangChain, LangGraph, Transformers, and OpenAI/Vertex/Bedrock SDKs
• Experience building AI agents, retrieval systems, tool‑calling workflows, or autonomous task orchestration
• Strong understanding of GenAI concepts including prompting, embeddings, RAG, evaluation metrics, hallucination detection, model selection, fine‑tuning, and context engineering
• Experience with cloud platforms (Azure, AWS, or GCP), containerization (Docker), and CI/CD systems for ML/AI
• Strong problem‑solving skills, system design abilities, and the capacity to translate business needs into scalable AI solutions
• Excellent verbal, written, and presentation skills

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