Senior AI/Data Science Consultant

Overview

In this role you will design and deliver production-grade Generative AI and Agentic AI solutions, working hybrid in London as part of EPAM’s Data & AI Practice. You lead technical delivery while also engaging stakeholders and shaping strategic AI initiatives. You tackle multi-agent systems and advanced RAG pipelines to deliver measurable business impact. This position blends hands-on engineering leadership with technical consulting, offering scope to influence architecture, governance, and pre-sales activities.

Responsibilities

• Design, build, and deploy Generative AI and Agentic AI solutions end-to-end from prototype to production
• Implement and optimize multi-agent systems using LangGraph, CrewAI, AutoGen, Semantic Kernel, or OpenAI Agents SDK
• Create orchestration patterns (planner/executor, supervisor/worker, tool-calling workflows)
• Build RAG pipelines with embeddings, chunking strategies, hybrid search, retrieval evaluation, and data grounding
• Architect reusable AI design patterns including human-in-the-loop workflows and multi-agent coordination models
• Apply agent integration standards (MCP, A2A, OpenAPI, REST, gRPC)
• Lead technical delivery of AI projects, managing timelines, dependencies, and stakeholder expectations
• Ensure delivery adheres to AI governance, data privacy, and regulatory frameworks
• Support pre-sales through technical solutioning, demos, and PoC development
• Apply observability and monitoring using Langfuse, Arize, and OpenTelemetry
• Contribute to architecture decisions and technical standards across engagements

Key requirements

• 6+ years in AI/ML engineering, data science, or applied AI in professional services or enterprise settings
• Proven experience building and deploying Generative AI and Agentic AI solutions into production
• Expertise in LLMs, advanced RAG strategies, prompt engineering, and model evaluation
• Hands-on experience with multi-agent orchestration frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel)
• Familiarity with orchestration protocols (MCP, A2A, OpenAPI)
• Strong Python development, CI/CD, DevOps, and cloud-native architectures
• Practical Azure experience (Azure OpenAI, Azure AI Foundry, AI Search) with familiarity of AWS or GCP
• Proficiency with containerized deployment, MLOps tooling (MLFlow, Airflow), and IaC practices
• Bachelor’s or Master’s degree in Computer Science, Data Science, or related field
• Experience with Azure AI Agent Service or Microsoft Agent Framework
• Familiarity with vector databases (Pinecone, Weaviate, Qdrant, Milvus)
• Knowledge of AI safety techniques such as guardrails, bias mitigation, and explainability
• Understanding of Intelligent Document Processing, data lineage, and distributed memory for agent systems
• Experience enabling responsible and auditable AI governance

• stakeholder engagement
• leadership and delivery management
• communication and collaboration
• LangGraph
• CrewAI
• AutoGen

…