EPAM Systems

EPAM GenAI Consultants are changemakers who bridge strategy and technology—applying agentic intelligence, RAG, and multimodal AI to transform how enterprises operate, serve users, and make decisions. 

Preferred Tech stack 

 Programming Languages 

• Python (*) 
• TypeScript 
• Rust 
• Mojo 
• Go 

 Fine-Tuning & Optimization 

• LoRA (Low-Rank Adaptation) 
• PEFT (Parameter-Efficient Fine-Tuning) 

Foundation & Open Models 

• OpenAI (GPT series), Anthropic Claude Family, Google Gemini, Grok (*, at least one of them ) 
• Llama 
• Falcon 
• Mistral 

Inference Engines  

• VLLM 

 Prompting & Reasoning Paradigms (*) 

• CoT (Chain of Thought) 
• ToT (Tree of Thought) 
• ReAct (Reasoning + Acting) 
• DSPy 

Multimodal AI Models 

• CLIP (*) 
• BLIP2 
• Whisper 
• LLaVA 
• SAM (Segment Anything Model) 

 Retrieval-Augmented Generation (RAG) 

• RAG (core concept) (*) 
• RAGAS (RAG evaluation and scoring) (*) 
• Haystack (RAG orchestration & experimentation) 
• LangChain Evaluation (LCEL Eval) 

Agentic Frameworks 

• CrewAI  (*) 
• AutoGen, AutoGPT, LangGraph, Semantic Kernel, LangChain (* at least  2 of them) 
• Prompt Tools:  PromptLayer, PromptFlow (Azure),  Guidance by Microsoft (* at least one of them) 

Evaluation & Observability 

• RAGAS – Quality metrics for RAG (faithfulness, context precision, etc.) (*) 
• TruLens – LLM eval with attribution and trace inspection (*) 
• EvalGAI – GenAI evaluation testbench 
• Giskard – Bias and robustness testing for NLP 
• Helicone – Real-time tracing and logging for LLM apps 
• HumanEval – Code generation correctness testing 
• OpenRAI – Evaluation agent orchestration 
• PromptBench – Prompt engineering comparison 
• Phoenix by Arize AI – Multimodal and LLM observability 
• Zeno – Human-in-the-loop LLM evaluation platform 
• LangSmith – LangChain observability and evaluation 
• WhyLabs – Data drift and model behavior monitoring 

Explainability & Interpretability (understanding) 

• SHAP 
• LIME 

Orchestration & Experimentation (*) 

• MLflow 
• Airflow 
• Weights & Biases (W&B) 
• LangSmith 

 Infrastructure & Deployment 

• Kubernetes 
• Amazon SageMaker 
• Microsoft Azure AI 
• Goggle Vertex AI  
• Docker 
• Ray Serve (for distributed model serving) 

Responsibilities 

• Lead GenAI discovery workshops with clients
• Design Retrieval-Augmented Generation (RAG) systems and agentic workflows
• Deliver PoCs and MVPs using LangChain, LangGraph, CrewAI , Semantic Kernel,  DSPy, RAGAS 
• Ensure Responsible AI principles in deployments (bias, fairness, explainability) 
• Support RFPs, technical demos, and GenAI architecture narratives 
• Reuse of accelerators/templates for faster delivery 
• Governance & compliance setup for enterprise-scale AI 
• Use of evaluation frameworks to close feedback loops 

Requirements 

• Consulting: Experience in exploring the business problem and converting it to applied AI technical solutions; expertise in pre-sales, solution definition activities 
• Data Science: 3+ years of hands-on experience with core Data Science, as well as knowledge of one of the advanced Data Science and AI domains (Computer Vision, NLP, Advanced Analytics etc.)   
• Engineering: Experience delivering applied AI from concept to production, familiarity, and experience with MLOps, Data, design of Data Analytics platforms, data engineering, and technical leadership 
• Leadership: Track record of delivering complex AI-empowered and/or AI-empowering programs to clients in a leadership position. Experience in managing and growing a team to scale up Data Science, AI, and ML capabilities is a big plus. 
• Excellent communication skills (active listening, writing and presentation), drive for problem solving and creative solutions, high EQ 
• Experience with LLMOps or GenAIOps tooling (e.g., guardrails, tracing, prompt tuning workflows) 
• Understanding of the importance of AI products evaluation is a must 
• Knowledge of cloud GenAI platforms (AWS Bedrock, Azure OpenAI, GCP Vertex AI) 
• Understanding of data privacy, compliance, and Governance in GenAI (GDPR, HIPAA, SOC2, RAI, etc.) 
• In-depth understanding of a specific industry or a broad range of industries. 

Technologies

• Python, Databricks, Azure ML, Big Data (Hadoop, Spark, Hive, etc.), AWS, Docker, Kubernetes, DB (Pl SQL, HQL, Mongo), Google (Vertex AI) or similar

Responsibilities

• Discover, envision and land Data Science, AI and Machine Learning opportunities alongside EPAM teams & clients
• Lead cross-functional EPAM and/or EPAM clients` teams through the journey of understanding business challenges and defining solutions leveraging AI, Data Science, Machine Learning and MLOps 
• Work with clients to deliver AI Products which provide value to end-users 
• Participate and drive EPAM competencies development, work on new EPAM offerings in AI, Data Science, ML and MLE space, as well as work on refining existing offerings 
• Bring your creative engineering mind to deliver real-life practical applications of Machine Learning 
• Work closely with DevOps practice on infrastructure and release planning 

Requirements

• Consulting: Experience in exploring the business problem and converging it to applied AI technical solutions; expertise in pre-sales, solution definition activities  
• Data Science: 3+ years of hands-on experience with core Data Science, as well as knowledge of one of the advanced Data Science and AI domains (Computer Vision, NLP, Advanced Analytics etc.)  
• Engineering: Experience delivering applied AI from concept to production, familiarity, and experience with MLOps, Data, design of Data Analytics platforms, data engineering, and technical leadership
• Leadership: Track record of delivering complex AI-empowered and/or AI-empowering programs to the clients in a leadership position. Experience managing and growing the team to scale up Data Science, AI &ML capability is a big plus
• Excellent communication skills (active listening, writing and presentation), drive for problem solving and creative solutions, high EQ

Nice to have

• One or more business domains expertise (e.g. CPG, Retail, Financial Services, Insurance, Healthcare/ Life Science)

We offer

• Work on a flexible schedule remotely or from any of our comfortable offices or coworking spaces in Ukraine
• Receive the necessary equipment to perform your work tasks
• Change projects and technology stacks within EPAM
• Gain experience in various business domains (Insurance, E-commerce, Healthcare, Finance, Travelling, Media, Artificial Intelligence, and more)
• Consider relocation options in over 30 countries worldwide
• Participate in volunteer, charity programs and communities (both technical and interest-based)
• You can plan your individual career path together with your manager
• Receive regular feedback from colleagues
• Improve your English for free with certified teachers (Speaking Clubs, client interview preparation courses, etc.)
• Get the opportunity to undergo free training and certification in AWS, GCP, or Azure Clouds
• Use the internal E-learn training program (18,200+ specialized training and mentoring programs)
• Access corporate accounts on LinkedIn Learning, Get Abstract and other partner resources
• Study at EPAM Solution Architecture School with the instructors who are practicing architects
• Develop as a leader, join Delivery Management, Resource Management, Leadership Essentials school and more
• Participate in internal communities (500+ meetups, technical discussions, brainstorming sessions, online events and conferences annually)
• Vacation and sick leave (including a sick leave without a medical certificate)
• A wide range of Voluntary Medical Insurance programs providing both medical treatment and various preventive options (including sports activities)
• Medical insurance for family members at corporate rates
• Company support during significant life events (childbirth or adoption, marriage, etc.)
• Support for psychological comfort: discounts on services from mental health specialists or coaches, thematic training
• E-kids program - a free programming language training program for EPAMers' children

Responsibilities

• Collaborate with customers and stakeholders to define and design efficient data schema and pipelines
• Perform ETL implementation, including transformations and data insertion into tables for analytics purposes
• Provide expertise in data warehousing and analytics to ensure optimized data structures
• Work on both the architectural design and actual system building
• Contribute to the development and implementation of data strategies and solutions
• Ensure the reliability and efficiency of data solutions for analytical purposes

Requirements

• Minimum of 10 years of practical hands-on experience in software development
• At least 2 years of experience as a Data Software Architect
• Expertise in SQL and Snowflake
• Proven experience in designing star or snowflake schemas for data warehousing and analytics
• Proficiency in data modeling and experience in cleaning, transforming, and preparing data for analysis
• Strong analytical and problem-solving abilities
• Excellent communication and interpersonal skills
• Fluent in English with a B2 level or higher

Nice to have

• Previous experience with a BI tool, especially Domo, is advantageous
• Familiarity with MongoDB is a valuable addition

Responsibilities

• Collaborate with customers and stakeholders to define and design efficient data schema and pipelines
• Perform ETL implementation, including transformations and data insertion into tables for analytics purposes
• Provide expertise in data warehousing and analytics to ensure optimized data structures
• Work on both the architectural design and actual system building
• Contribute to the development and implementation of data strategies and solutions
• Ensure the reliability and efficiency of data solutions for analytical purposes

Requirements

• Minimum of 10 years of practical hands-on experience in software development
• At least 2 years of experience as a Data Software Architect
• Expertise in GCP
• Expertise in SQL and Snowflake
• Proven experience in designing star or snowflake schemas for data warehousing and analytics
• Proficiency in data modeling and experience in cleaning, transforming, and preparing data for analysis
• Strong analytical and problem-solving abilities
• Excellent communication and interpersonal skills
• Fluent in English with a B2 level or higher

Nice to have

• Previous experience with a BI tool, especially Domo, is advantageous
• Familiarity with MongoDB is a valuable addition

We are currently seeking a Solution Architect who specializes in data-driven projects to become a part of our Data Practice team in Ukraine.

Responsibilities

• Architect data analytics solutions by leveraging the big data technology stack
• Develop and present detailed technical solution architecture documents
• Collaborate with business stakeholders to define solution requirements and explore case studies/scenarios for future solutions
• Perform solution architecture reviews/audits, compute and present ROI
• Manage the implementation of solutions from setting project requirements and objectives to the solution “go-live”
• Engage in the entire spectrum of pre-sale activities, including direct communication with customers, RFP processing, crafting implementation proposals, and solution architecture presentations to clients, as well as participation in technical discussions with client representatives
• Construct and adhere to a personal education plan in technology stack and solution architecture
• Develop a robust understanding of industry trends and best practices
• Participate in the acquisition of new clients to expand EPAM’s business in the big data sector

Requirements

• Minimum of 7 years' experience required
• Proficiency in hands-on roles as a Big Data Architect with a strong design/development background in Java, Scala, or Python
• Background in delivering data analytics projects and architecture guidelines
• Skills in big data solutions, both on-premises and on cloud platforms such as Amazon Web Services, Microsoft Azure, and Google Cloud
• Production project experience with at least one big data technology is essential
• Batch processing expertise: Hadoop, MapReduce/Spark/Hive
• Understanding of NoSQL databases: Cassandra, HBase, Accumulo, Kudu
• Familiarity with Agile development methodology, particularly Scrum
• Competency in client communication and pre-sales business-consulting to large enterprise environments
• Experience within a consulting firm and pre-sales backgrounds are highly desirable
• Upper-Intermediate level in English, both spoken and written (B2+)

This role is well-regarded for leaders with a strong mathematics background and a desire to work hands-on with Data Science, AI, ML and could be the perfect opportunity to join EPAM as a Senior Manager - Data Science Consultant!
 

Kindly note that this role supports remote work, but only from within Ukraine.

#LI-DNI#LI-VC3

Technologies

• Python, Databricks, Azure ML, Big Data (Hadoop, Spark, Hive, etc.), AWS, Docker, Kubernetes, DB (Pl SQL, HQL, Mongo), Google (Vertex AI) or similar

Responsibilities

• Discover, envision and land Data Science, AI and Machine Learning opportunities alongside EPAM teams & clients
• Lead cross-functional EPAM and/or EPAM clients` teams through the journey of understanding business challenges and defining solutions leveraging AI, Data Science, Machine Learning and MLOps 
• Work with clients to deliver AI Products which provide value to end-users 
• Participate and drive EPAM competencies development, work on new EPAM offerings in AI, Data Science, ML and MLE space, as well as work on refining existing offerings 
• Bring your creative engineering mind to deliver real-life practical applications of Machine Learning 
• Work closely with DevOps practice on infrastructure and release planning 

Requirements

• Consulting: Experience in exploring the business problem and converging it to applied AI technical solutions; expertise in pre-sales, solution definition activities  
• Data Science: 3+ years of hands-on experience with core Data Science, as well as knowledge of one of the advanced Data Science and AI domains (Computer Vision, NLP, Advanced Analytics etc.)  
• Engineering: Experience delivering applied AI from concept to production, familiarity, and experience with MLOps, Data, design of Data Analytics platforms, data engineering, and technical leadership
• Leadership: Track record of delivering complex AI-empowered and/or AI-empowering programs to the clients in a leadership position. Experience managing and growing the team to scale up Data Science, AI &ML capability is a big plus
• Excellent communication skills (active listening, writing and presentation), drive for problem solving and creative solutions, high EQ

Nice to have

• One or more business domains expertise (e.g. CPG, Retail, Financial Services, Insurance, Healthcare/ Life Science)