AI /Data Science Consultant
First seen by Alion on Oct 12, 2026. EPAM Systems scores D on the Alion truth index.
About the Role
We are seeking an AI & Data Science Consultant to drive the discovery, strategy, and end-to-end delivery of advanced AI initiatives for EPAM’s global clients. This role is a unique hybrid, blending strategic business consulting with deep practical engineering. You will collaborate directly with client executives and EPAM’s delivery teams to transform complex business challenges into scalable, production-ready AI, Machine Learning, and Agentic solutions.
Key Responsibilities
Consult & Strategize: Lead client-facing discovery sessions and workshops to identify high-value AI opportunities, define MVP scopes, and map out strategic delivery roadmaps.
Design & Architect: Shape scalable AI products incorporating Generative AI, RAG, multi-agent frameworks, semantic layers, and advanced analytics.
Bridge the Collaboration: Serve as the central link connecting business stakeholders, data scientists, ML engineers, DevOps, and product teams.
Drive Delivery & MLOps: Apply a strong engineering mindset to ensure solutions are secure, production-ready, and smoothly integrated into cloud infrastructures.
Support Business Growth: Partner on pre-sales activities, solution estimation, and proposal development while contributing to EPAM’s internal AI and MLOps offerings.
Requirements
Professional Experience: 3+ years of hands-on experience in Data Science, ML, or Applied AI, with a proven track record of bringing models from concept to production.
Consulting & Communication: Strong active listening, presentation, and storytelling skills; ability to confidently translate complex AI concepts for C-level stakeholders.
AI & Agentic Tech Stack: Experience with RAG, LLM applications, multi-agent orchestration, and tools like LangChain, LangGraph, CrewAI, LlamaIndex, or Vector DBs.
Data & Architecture: Understanding of Semantic Layers, metadata, knowledge graphs, and data platforms (such as Databricks or Snowflake).
Engineering & MLOps: Familiarity with the production model lifecycle, MLOps/LLMOps pipelines, and cloud platform integrations.
Nice to Have
Experience mentoring cross-functional AI teams or driving enterprise-scale GenAI adoption.
Practical knowledge of cloud infrastructure and DevOps tooling.
Insights into AI governance and ethical AI frameworks.
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