AI & Data Science Consultant

We are seeking an AI & Data Science Consultant to lead discovery envisioning and delivery of AI Data Science Machine Learning Agentic AI and Semantic Layer opportunities alongside EPAM teams and clients. This position blends strategic consulting with practical engineering expertise to convert business challenges into scalable production-ready AI solutions that generate measurable value.

Please note that this position supports remote work but only from within Ukraine.

• Lead discovery envisioning and delivery of AI Data Science Machine Learning Agentic AI and Semantic Layer opportunities alongside EPAM teams and clients
• Run client-facing consulting engagements to understand business challenges pinpoint high-value AI use cases and translate them into practical technical solutions
• Design and shape AI products that integrate Data Science ML Generative AI Semantic Layer RAG agents analytics and MLOps to deliver measurable business value
• Serve as a bridge between business stakeholders data scientists ML engineers data engineers architects DevOps and product teams
• Define solution concepts target architectures delivery roadmaps MVP scopes and productionization approaches for AI-enabled products
• Support pre-sales discovery workshops solution definition estimation and proposal development for AI ML and Data Science opportunities
• Contribute to EPAM offerings across AI Data Science ML Engineering MLOps Agentic AI Semantic Layer RAG and AI governance
• Apply a strong engineering mindset to turn AI ideas into reliable scalable secure and production-ready solutions
• Partner closely with DevOps Cloud Data Engineering and Architecture practices on infrastructure deployment observability release planning and operational readiness
• Mentor and guide cross-functional teams fostering capability growth in AI Data Science ML Engineering and applied GenAI

• 3 years of hands-on experience in Data Science Machine Learning or Applied AI
• Background in exploring business problems identifying AI opportunities and converting them into applied AI Data Science ML or GenAI solutions
• Expertise in pre-sales solution shaping and discovery workshops including stakeholder interviews roadmap definition and proposal preparation
• Capability to explain complex AI concepts to business and technical audiences including C-level stakeholders
• Understanding of supervised and unsupervised learning model evaluation feature engineering experimentation and production model lifecycle
• Proficiency in at least one advanced AI domain: NLP Computer Vision Forecasting Optimization Advanced Analytics Recommendation Systems or Predictive Modeling
• Knowledge of RAG LLM applications prompt engineering evaluation hallucination reduction and grounding techniques
• Competency in agentic architectures multi-agent workflows tool use orchestration memory and control-plane concepts
• Skills in Semantic Layer concepts business metrics modeling metadata knowledge graphs ontology/taxonomy design and enterprise context management
• Familiarity with LangChain LangGraph and LlamaIndex; CrewAI DSPy and Semantic Kernel; Vector DBs MLflow Kubeflow Databricks or Snowflake
• Proven record of delivering AI/ML solutions from concept to production with exposure to MLOps LLMOps and CI/CD for ML plus model monitoring observability and data pipelines
• Understanding of data platforms data engineering data quality governance and scalable analytics architectures including APIs cloud platforms and containerized deployment
• Demonstrated history of leading complex AI ML or data-driven programs in a consulting or client-facing role
• Excellent communication active listening writing storytelling and presentation skills combined with problem-solving mindset creativity ownership high EQ and ability to operate in ambiguous environments

• Background in managing mentoring or scaling AI/Data Science teams
• Flexibility to use cloud and DevOps tooling for infrastructure security release planning and production readiness
• Demonstrated contributions to AI governance practices and enterprise-scale GenAI adoption