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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist (R-19646) - **Company:** Dun & Bradstreet - **Location:** Short Hills, United States (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Software Applications, Automated Storage and Retrieval Systems, Microsoft Azure, Program Optimization, Computer Programming, Continuous Integration, Data Validation, Distributed Data Store, Distributed Systems, Iterative and Incremental Development, Python (Programming Language), Machine Learning, Search Technologies, Software Engineering, Delivery Pipeline, Large Language Models, Model Validation, Generative AI, Containerization, Pyspark, Machine Learning Operations, Api Design, GPT, Docker - **Published:** August 24, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/peld1snsxc ## About the Role * 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 ## 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, 2. 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. 3. 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. 4. 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. 5. MLOps / LLMOps * Contribute to CI/CD workflows for AI models-deployment, testing, monitoring. * Implement governance, guardrails, and reusable GenAI frameworks. 6. 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. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [Beyond GPT: Building Unified GenAI Platforms for the Enterprise of Tomorrow](https://www.wearedevelopers.com/videos/1525-beyond-gpt-building-unified-genai-platforms-for-the-enterprise-of-tomorrow) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)