Senior Data Scientist (Agentic AI + Azure)

• Strong Python proficiency with hands-on experience using Azure AI Foundry and LangChain or LangGraph for building GenAI applications
• Solid experience with RAG pipeline design, including Azure AI Search, vector databases, embedding models, and retrieval optimization strategies
• Hands-on experience with Azure ML for model training, evaluation, and deployment, including pipeline automation and model monitoring
• Practical experience with prompt engineering, model evaluation tools (RAGAS, TruLens, or similar), and responsible AI practices
• Experience with agentic AI frameworks and multi-agent orchestration patterns using LangGraph, Semantic Kernel, or Azure AI Agent Service
• Solid understanding of Azure cloud architecture, data services, and security best practices for enterprise AI workloads
• Master's degree in Computer Science, Applied Mathematics, Data Science, Engineering, or a related field
• Upper-intermediate or higher proficiency in English, both spoken and written

About the Role

In this role, you will design and deliver enterprise-grade Generative AI solutions — from RAG pipelines and agentic workflows to fine-tuned foundation models – within SoftServe's AI and Data Science Center of Excellence, a team of 170+ experts including Data Scientists, ML Engineers, and Architects. Working at the intersection of cutting-edge GenAI research and production engineering, you'll translate complex business challenges into scalable AI systems that drive measurable impact for world-leading clients.

SoftServe is an equal opportunity employer. Qualified applicants will receive consideration regardless of race, color, ancestry, ethnicity, national origin, religion, sex, sexual orientation, gender identity or expression, age, citizenship, disability, health condition, marital or family status, veteran status, or any other characteristic protected by applicable law.

,[Design and develop end-to-end Generative AI solutions on Azure — including RAG pipelines, agentic workflows, and conversational AI systems — ensuring alignment with client business objectives and production standards, Experiment with, evaluate, and fine-tune foundation models to optimize performance, accuracy, and cost efficiency for diverse enterprise use cases, Build scalable data pipelines for GenAI workloads, including document ingestion, chunking strategies, embedding generation, and vector index management, Collaborate with ML Engineers, Architects, and client stakeholders to ensure reliable deployment of GenAI solutions and smooth handoff from proof-of-concept to production, Design and implement multi-agent AI systems and agentic orchestration patterns that automate complex business processes at scale, Apply prompt engineering, model evaluation frameworks, and guardrail mechanisms to ensure responsible, accurate, and consistent AI output in production, Contribute to CoE best practices, reusable solution accelerators, and internal knowledge-sharing] Requirements: Python, Azure, AI, Azure ML, Azure Cloud, Security, Degree