GenAI Engineer

We are seeking a GenAI Engineer to lead the end-to-end development, deployment and operation of enterprise-grade AI-powered applications. The role combines backend engineering, LLM integration, cloud infrastructure and AI platform operations to deliver scalable GenAI solutions in production environments. You will work closely with AI/DS, Product and DevOps teams to build and scale AI-driven applications, ensuring reliability, observability, performance optimization and operational excellence across the full AI SDLC. The role also includes contributing to GenAI-assisted development practices, scaling enterprise AI SDLC processes, supporting AI Beauty Chat initiatives through agentic micro-pod delivery models and performing System Steward responsibilities across AI platform initiatives.

Responsibilities

Design, develop, deploy and maintain backend services for AI/LLM-powered applications

Own E2E delivery of GenAI features from implementation to production support

Integration and operation of LLM APIs (e.g. OpenAI) in enterprise production environments

Development of APIs, orchestration layers and microservices supporting agentic AI workflows

Optimization of LLM systems for latency, resiliency, retries, fallbacks and cost efficiency

Implementation of CI/CD pipelines, observability, monitoring and logging for AI services

Collaboration with AI/DS, Product, DevOps and platform teams to streamline delivery and improve reliability

Work with Azure cloud environments and distributed systems (Redis, Kafka, SQL/NoSQL)

Support for MCP integrations, agentic memory initiatives and AI orchestration frameworks

Drive GenAI-assisted development practices and scale AI SDLC processes

Contribution to AI Beauty Chat delivery through agentic micro-pod execution models

Execution of System Steward responsibilities in agentic micro-pods

Requirements

2+ years of Python backend engineering experience

Expertise in building and operating production-grade GenAI/LLM applications end-to-end

Hands-on experience with OpenAI or other LLM APIs in production

Proficiency in prompt engineering, agentic workflows and orchestration patterns

Capability to handle LLM operational challenges: latency, retries, fallbacks, observability and cost optimization

Strong understanding of scalable backend and distributed system architecture

Skills in CI/CD, DevOps workflows and Azure cloud environments

Background in applying GenAI across the SDLC (AI-assisted development, testing, deployment and delivery workflows)

Working knowledge of SQL/NoSQL databases, Redis and Kafka

Familiarity with Databricks and MCP

We offer

International projects with top brands

Work with global teams of highly skilled, diverse peers

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

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