About The Role

Shape the Future with Dun & Bradstreet

At Dun & Bradstreet, we believe data has the power to create a better tomorrow. As a global leader in business decisioning data and analytics, we help companies worldwide grow, manage risk, and innovate. Since 1841, businesses have trusted us to turn uncertainty into opportunity. We’re a diverse, global team that values creativity, collaboration, and bold ideas. Are you ready to make an impact and help shape what’s next? Join us! Explore opportunities at dnb.com/careers.

Key Responsibilities

Design, implement, and deploy AI agents and generative AI solutions using Python, with hands-on use of LangChain and related orchestration frameworks
Build modular, scalable, and reusable agent components, including memory, planning, tool use, retrieval, and multi-modal capabilities
Develop retrieval-augmented generation (RAG) solutions using embeddings, vector databases, and enterprise data sources
Integrate open-source and proprietary large language models and evaluate the appropriate model for each business use case
Collaborate with Research, Managed Services, product, data, and technology teams to translate business needs into practical AI solutions
Prototype and evaluate agent behaviors, prompt strategies, learning approaches, and tool-calling workflows
Build and maintain scalable data pipelines and APIs that support AI development, evaluation, deployment, and monitoring
Benchmark solution performance, identify bottlenecks, and implement improvements for quality, reliability, latency, and cost
Implement observability, testing, evaluation, guardrails, and monitoring to support reliable and responsible AI delivery
Maintain CI/CD and MLOps/LLMOps practices for controlled and repeatable delivery of AI capabilities
Follow data governance, security, privacy, and intellectual property requirements throughout the solution lifecycle
Communicate technical concepts clearly to both technical and non-technical stakeholders and contribute to knowledge sharing

Key Skills

Bachelor's degree in computer science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field; advanced degree preferred
5 to 8 years of relevant experience in AI/ML engineering, software engineering, data science, or a related technical field
Demonstrated hands-on experience implementing AI solutions using LangChain. Experience should include building and deploying working solutions, not only theoretical knowledge
Strong programming experience in Python and experience developing APIs, reusable services, and data pipelines
Hands-on knowledge of agentic AI patterns, RAG, prompt engineering, embeddings, vector databases, model evaluation, and tool calling
Experience with one or more cloud platforms such as Azure, AWS, or GCP and containerization technologies such as Docker or Kubernetes
Experience with CI/CD, MLOps, or LLMOps practices, including testing, deployment, monitoring, and controlled releases
Ability to explain technical decisions, solve ambiguous business problems, and collaborate effectively with cross-functional stakeholders
Ownership mindset, curiosity, proactive problem solving, and a commitment to continuous learning and collaboration
Fluency in English and any additional language relevant to the working market, where applicable