We are seeking a Lead Data Software Engineer to come on board with our team.

This position centers on developing and maintaining data infrastructure that fuels AI-powered products and intelligent agent systems. You'll get the chance to engage with state-of-the-art technologies and help shape scalable, dependable platforms within a cooperative setting.

Responsibilities

• Plan, build, and support data ingestion and processing pipelines that supply RAG systems, covering the management of unstructured data, images, videos, metadata, and permissions
• Oversee and fine-tune vector database infrastructure, such as Amazon Kendra alongside an active migration toward OpenSearch
• Build evaluation datasets and performance measurement frameworks tailored to agents
• Establish monitoring and observability pipelines for AI workloads, including dashboards for latency, quality, and cost
• Roll out data governance, privacy guardrails, and quality controls for AI training and inference data
• Back A/B testing and experimentation infrastructure used to evaluate agent iterations
• Work jointly with Backend AI engineers on data schemas and embedding approaches

Requirements

• At least 5 years of data engineering background, including direct work with AI/ML data infrastructure
• A minimum of one year guiding and managing development teams
• Solid Python expertise for crafting data pipelines, ETL workflows, and backend automation scripts
• Practical production experience with vector databases, covering schema design and index management for Amazon Kendra or OpenSearch
• Thorough grasp of search and retrieval concepts, including embedding models, chunking techniques, and retrieval optimization
• Working familiarity with AWS services like S3, Glue, Athena, and Kinesis (or equivalents), as well as Docker and distributed data environments
• Experience treating data quality practices such as monitoring, validation, and lineage tracking as operational standards
• Background in defining AI/ML evaluation metrics and setting up systematic tracking using evaluation frameworks
• English language proficiency in writing and speaking at B2+ level or higher

Nice to have

• Exposure to LangSmith, RAGAS, or custom-built evaluation framework approaches
• Experience with multi-modal data processing involving unstructured text, images, and videos, together with related governance
• Hands-on participation in LLM fine-tuning data preparation
• Familiarity with observability tools tightly integrated with AI calls, such as Langfuse or Arize
• Background in constructing streaming data pipelines with technologies like Kafka or Kinesis

We offer

• International projects with top brands
• Work with global teams of highly skilled, diverse peers
• Healthcare benefits
• Employee financial programs
• Paid time off and sick leave
• Upskilling, reskilling and certification courses
• Unlimited access to the LinkedIn Learning library and 22,000+ courses
• Global career opportunities
• Volunteer and community involvement opportunities
• EPAM Employee Groups
• Award-winning culture recognized by Glassdoor, Newsweek and LinkedIn