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. Responsibilities 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 Requirements 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 Nice to have 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