{"id":3151489,"url":"https://alion.io/job/dun-bradstreet-senior-data-scientist-generative-ai","title":"Senior Data Scientist - Generative AI","company":{"id":2406,"name":"Dun & Bradstreet","domain":"dnb.com","url":"https://alion.io/company/dnb","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":82,"open_postings":9,"ghost_share":0,"stale_share":0.667,"repost_share":0.111,"time_to_fill_p50_days":45,"computed_at":"2026-10-29T05:49:05Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India","Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":24000,"max_usd":53000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Context Engineering","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Hallucination","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"OpenAI","optional":false},{"name":"Platform Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Transformers","optional":false},{"name":"Machine Learning","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-23T11:26:48Z","employer_posted_date":null,"last_verified_at":"2026-10-23T11:26:48Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"The Role : \n\nWe are looking for an experienced AI Engineer to design, build, and operationalize AI driven solutions for our global Analytics organization.\n\nThe 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.\n\nYou will work closely with data scientists, MLOps engineers, and business stakeholders to build intelligent, production grade systems that power next generation analytics and decisioning capabilities - aligned with the organization's GenAI roadmap.\n\nKey Responsibilities : \n\n1. Agent Development & Architecture : \n\n- Build agentic workflows using LangChain/LangGraph and similar frameworks.\n\n- Develop autonomous agents for data validation, reporting, document processing, and domain workflows.\n\n- Deploy scalable, resilient agent pipelines with monitoring and evaluation.\n\n2. GenAI Application Engineering : \n\n- Develop GenAI applications using models like GPT, Gemini, and LLaMA.\n\n- Implement RAG, vector search, prompt orchestration, and model evaluation.\n\n- Partner with data scientists to productionize POCs.\n\n3. Data & Platform Engineering : \n\n- Build distributed data pipelines (Python, PySpark).\n\n- Develop APIs, SDKs, and integration layers for AI-powered applications.\n\n- Optimize systems for performance and scalability across cloud/hybrid environments.\n\n4. MLOps / LLMOps : \n\n- Contribute to CI/CD workflows for AI models - deployment, testing, monitoring.\n\n- Implement governance, guardrails, and reusable GenAI frameworks.\n\n5. Collaboration & Stakeholder Engagement : \n\n- Work with analytics, product, and engineering teams to define and deliver AI solutions.\n\n- Participate in architecture reviews and iterative development cycles.\n\n- Support knowledge sharing and internal GenAI capability building.\n\nKey Requirements : \n\n- 7+ years of experience in AI/ML engineering, data science, or software engineering, with atleast 2 years focused on GenAI.\n\n- Strong programming expertise in Python, distributed computing using PySpark, and API development.\n\n- Hands on experience with LLM frameworks (LangChain, LangGraph, Transformers, OpenAI/Vertex/Bedrock SDKs).\n\n- Experience developing AI agents, retrieval pipelines, tool calling structures, or autonomous task orchestration.\n\n- Solid understanding of GenAI concepts : prompting, embeddings, RAG, evaluation metrics, hallucination identification, model selection, fine tuning, context engineering.\n\n- Experience with cloud platforms (Azure/AWS/GCP), 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