Senior GenAI Engineer — AI Platform & Backend Leader

EPAM Systems; Mexico; On-site; MXN 900,000 - 1,300,000; Full time

Benefits offered by this job

Healthcare benefits

Employee financial programs

Unlimited LinkedIn Learning access

Volunteer opportunities

Job summary

EPAM Systems is seeking a Senior nAI Engineer to own the end-to-end lifecycle of GenAI-powered applications. You will integrate LLM APIs, build backend services, and ensure reliability, observability, and cost efficiency across the AI SDLC.

You will collaborate with AI/DS, Product, DevOps, and platform teams, leveraging Azure and distributed systems like Redis and Kafka to deliver scalable solutions in a multinational environment.

Qualifications

• At least 3 years of relevant experience.
• Experience delivering and running production-grade GenAI/LLM applications.
• Hands-on work with OpenAI or comparable LLM APIs.

Responsibilities

• Craft, build, launch, and support backend services that drive AI/LLM-powered applications.
• Assume complete responsibility for GenAI feature delivery from development to production support.
• Link and administer LLM APIs inside enterprise production settings.
• Build APIs, orchestration layers, and microservices to power agentic AI workflows.
• Configure CI/CD pipelines with observability, monitoring, and logging for AI services.
• Collaborate with AI/DS, Product, DevOps, and platform groups to improve delivery and reliability.
• Work within Azure cloud settings and distributed systems (Redis, Kafka, SQL/NoSQL).
• Support MCP integrations, agentic memory efforts, and AI orchestration frameworks.
• Promote GenAI-assisted development approaches and scale the client’s AI SDLC.

Skills

Python backend

GenAI/LLM expertise

OpenAI APIs

Distributed systems

CI/CD / DevOps

Azure cloud

Prompt engineering

Backend architecture

English communication

Tools

Redis

Kafka

SQL/NoSQL

Job description

EPAM Systems is seeking a Senior nAI Engineer to own the end-to-end lifecycle of GenAI-powered applications. You will integrate LLM APIs, build backend services, and ensure reliability, observability, and cost efficiency across the AI SDLC.

You will collaborate with AI/DS, Product, DevOps, and platform teams, leveraging Azure and distributed systems like Redis and Kafka to deliver scalable solutions in a multinational environment.