Senior Data Scientist (Agentic AI + Azure)

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.

Responsibilities

• 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

• 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