Generative AI Operations Engineer (GenAI Ops)

EPAM Systems; Turkey; On-site; TRY 300,000 - 460,000; Full time

Benefits offered by this job

Private health insurance

English courses

Professional development support

Mentoring programs

Learning platform access

Job summary

EPAM Systems seeks a Generative AI Operations (GenAI Ops) Engineer to join its forward-thinking team in Turkey. You will build, deploy, and maintain the operational backbone for LLMs and AI agents, ensuring scalable, reliable GenAI services across leading clouds.

Collaborate with data scientists, ML engineers, and software developers to operationalize AI systems. You will implement automated CI/CD pipelines, manage multi-agent workflows, and optimize performance, governance, and security across

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field, or equivalent practical experience.
• At least 3 years in a DevOps, SRE, or MLOps role with cloud infra focus.
• Hands-on cloud experience (AWS/GCP/Azure).
• Strong CI/CD experience (Jenkins, GitLab CI, or cloud-native).
• Python or Bash scripting proficiency.
• Experience with IaC tools (AWS CDK, CloudFormation, Terraform).
• Familiarity with GenAI services (AWS Bedrock, Azure AI Foundry, Vertex AI).
• Understanding of LLM architecture and operational challenges.
• Experience with Hugging Face, OpenAI, LangChain.
• Monitoring/observability tools (Prometheus, Grafana).
• ML experiment tracking (MLflow, Weights & Biases).
• Docker and Kubernetes expertise.
• Fluent English (B2+).

Responsibilities

• Design, implement, and maintain automated CI/CD pipelines for training, evaluating, and deploying LLMs and AI agents.
• Deploy and manage multi-agent AI systems with seamless agent communication.
• Integrate AI agents with external tools via interoperability standards.
• Leverage AI development tools to accelerate infrastructure code, testing, and troubleshooting in cloud environments.
• Define GenAI infra using IaC tools (Terraform, CDK, CloudFormation).
• Implement monitoring, logging, and tracing for model and agent performance and system health.
• Design scalable architectures for model serving and inference with performance and cost efficiency in mind.
• Apply security best practices and ensure compliance for GenAI infra and data.

Skills

DevOps

GenAI/MLOps

Python

English

LLMs

Multi-agent systems

Education

Bachelor's degree in CS/Engineering

Tools

Jenkins

GitLab CI

Terraform

AWS CDK

CloudFormation

Docker

Kubernetes

Prometheus

Grafana

MLflow

Weights & Biases

Hugging Face

OpenAI

LangChain

MCP

Vertex AI

Bedrock

Job description

We are seeking a highly motivated and experienced Generative AI Operations (GenAI Ops) Engineer to join our forward-thinking team.

In this position, you will play a key role in building, deploying, and maintaining the operational backbone for advanced generative AI models and services. You will collaborate with data scientists, machine learning engineers, and software developers to ensure our GenAI applications—including complex, multi-agent systems—are scalable, reliable, and efficient across leading cloud platforms. If you are passionate about operationalizing large-scale AI systems and eager to make a meaningful impact, we want to hear from you.

Responsibilities

• Design, implement, and maintain automated CI/CD pipelines for training, evaluating, and deploying large language models (LLMs) and AI agents
• Deploy and manage sophisticated multi-agent AI systems, ensuring seamless agent-to-agent communication and collaboration for automating complex business processes
• Integrate AI agents with external tools and APIs, using open standards like Model Context Protocol (MCP) to ensure interoperability and security
• Utilize AI-powered development tools to accelerate infrastructure code, testing, and troubleshooting in cloud environments
• Define and manage GenAI infrastructure using cloud-native or cloud-agnostic Infrastructure as Code (IaC) tools such as Terraform
• Implement monitoring and logging solutions to track model and agent performance, resource usage, and system health, including tracing agent actions and multi-step conversational flows
• Design and optimize scalable architectures for model serving and inference, focusing on performance and cost-effectiveness
• Apply security best practices and ensure compliance with industry standards and regulations for GenAI infrastructure and data

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
• At least 3 years of experience in a DevOps, SRE, or MLOps role with a focus on cloud infrastructure
• Hands‑on experience with cloud services from major providers such as AWS, Google Cloud, or Azure
• Strong background in building and managing CI/CD pipelines using tools like Jenkins, GitLab CI, or cloud-native solutions
• Proficiency in at least one scripting language such as Python or Bash
• Experience with Infrastructure as Code (IaC) tools like AWS CDK, CloudFormation, or Terraform
• Familiarity with cloud-native GenAI services such as AWS Bedrock, Azure AI Foundry, or Google Vertex AI
• Understanding of the architecture and operational challenges of Large Language Models (LLMs)
• Practical experience with generative AI frameworks including Hugging Face, OpenAI, or LangChain
• Experience with monitoring and observability tools for AI/ML systems such as Prometheus or Grafana
• Experience with ML experiment tracking and versioning tools like MLflow or Weights & Biases
• Hands‑on experience with containerization and orchestration technologies such as Docker and Kubernetes
• Fluent English communication skills at a B2+ level

Nice to have

• Master’s degree or PhD in Computer Science, AI, Machine Learning, or a related discipline
• Experience designing or managing multi‑agent systems or orchestrated AI workflows
• Relevant certifications in cloud or DevOps technologies
• Strong analytical and problem‑solving skills with the ability to thrive in a fast‑paced, collaborative environment

We offer

• CONTINUOUS UPSKILLING, LEARNING & DEVELOPMENT
  • Diversity of tasks and projects
  • Assessment center for objective review of competency level
  • Personal development plan
  • Mentoring programs and leadership development
  • Certification and professional development support
  • Access to learning platforms including more than 2,500 internal courses
  • English courses taught by certified teachers
• CORPORATE BENEFITS
  • Extra leave days
  • Referral bonuses
• COMPENSATION PACKAGE
  • Competitive compensation paid in USD
  • Regular salary and performance reviews
• MEDICAL & HEALTHCARE
  • Private health insurance
  • Well‑being events
• WORKING ENVIRONMENT
  • Recreation areas and kitchens
  • Tea, coffee and snacks
  • Sports equipment and game consoles
  • IT Equipment
  • Microsoft’s Software Assurance Home Use Program (HUP)

EPAM is global leader in AI transformation engineering and integrated consulting, serving Forbes Global 2000 companies and ambitious startups. With over thirty years of expertise in custom software, product and platform engineering, we empower our clients to become AI-Native enterprises, driving measurable value from innovation and digital investments.