Senior Generative AI Operations (GenAI Ops) Engineer

EPAM Systems; Turkey; On-site; TRY 5,744,000 - 8,617,000; Full time

Benefits offered by this job

Continuous upskilling

Diversity of tasks

Professional development support

Private health insurance

English courses

Recognition programs

Job summary

EPAM Systems is seeking a highly motivated GenAI Operations Engineer to build, deploy, and maintain the operational infrastructure for generative AI models and services. You will work with data scientists, ML engineers, and software developers to ensure GenAI applications are scalable, reliable, and efficient across major cloud platforms.

You will design and manage CI/CD for LLMs, orchestrate multi-agent workflows, and integrate tools securely while leveraging Terraform and other IaC practices.

Qualifications

• 3+ years in a DevOps, SRE or MLOps role with a focus on cloud infrastructure and a background in cloud services (AWS, GCP, Azure).
• Proficient in building and managing CI/CD pipelines (Jenkins, GitLab CI or cloud-native) and scripting language (Python, Bash).
• Familiar with IaC tools (AWS CDK, CloudFormation, Terraform) and containerization/orchestration (Docker, Kubernetes).
• Experience deploying and operating LLM inference (vLLM, Triton, TGI, Ray Serve, KServe/Seldon).
• Hands-on with LLM/app tracing and metrics (OpenTelemetry + Langfuse, Arize Phoenix, WhyLabs) and building eval pipelines.
• Skilled in operating retrieval pipelines: embedding generation, indexing/refresh strategies, vector DBs (Pinecone, Weaviate, Milvus, FAISS) and relevance monitoring.
• Experience with multi-agent workflows (LangGraph, CrewAI, AutoGen-like), state management, retries, rate limits, tool-failure handling and auditing.
• Experience implementing guardrails: secrets isolation, tool/API permissions, prompt-injection defenses, data leakage prevention, PII redaction and policy enforcement.

Responsibilities

• Build and Manage CI/CD Pipelines: Design, implement and maintain robust, automated CI/CD pipelines for training, evaluating and deploying large language models (LLMs) and AI agents.
• Orchestrate Agentic AI Workflows: Design, deploy and manage sophisticated multi-agent systems Ensure seamless Agent-to-Agent (A2A) communication and collaboration between specialized agents to automate complex business processes
• Manage Tool Integration: Implement and manage secure, scalable integrations between AI agents and external tools/APIs, leveraging open standards like the Model Context Protocol (MCP) to ensure interoperability
• Leverage AI-Powered Development: Utilize AI-powered development tools to accelerate the entire software development lifecycle from writing infrastructure code and tests to troubleshooting operational issues in cloud environments
• Infrastructure as Code (IaC): Utilize cloud-native IaC services or cloud-agnostic tools like Terraform to define and manage the infrastructure required for GenAI workloads
• Model Monitoring and Observability: Implement comprehensive monitoring and logging solutions to track model and agent performance, resource utilization and system health For agentic systems, this includes tracing the agent's actions and logging the multi-step conversational flow
• Scalability and Performance Optimization: Design and implement scalable architectures for model serving and inference Continuously optimize the performance and cost-effectiveness of our GenAI services
• Security and Compliance: Implement and enforce security best practices for our GenAI infrastructure and data Ensure compliance with industry standards and regulations

Skills

CI/CD pipelines

Cloud platforms AWS/GCP/Azure

IaC Terraform

Docker Kubernetes

Python Bash scripting

LLM inference deployment

Monitoring observability

Security compliance

Education

Master's degree or PhD in CS/AI

Tools

Jenkins

GitLab CI

Terraform

AWS CDK / CloudFormation

Job description

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

In this role, you will be at the forefront of the AI revolution, responsible for building, deploying, and maintaining the operational infrastructure for our cutting-edge generative AI models and services. You will work closely with data scientists, machine learning engineers and software developers to ensure our GenAI applications- especially complex multi-agent systems- are scalable, reliable and efficient across major cloud platforms. If you are passionate about operationalizing large-scale AI systems and want to make a significant impact, this is the role for you.

Responsibilities

• Build and Manage CI/CD Pipelines: Design, implement and maintain robust, automated CI/CD pipelines for training, evaluating and deploying large language models (LLMs) and AI agents
• Orchestrate Agentic AI Workflows: Design, deploy and manage sophisticated multi-agent systems Ensure seamless Agent-to-Agent (A2A) communication and collaboration between specialized agents to automate complex business processes
• Manage Tool Integration: Implement and manage secure, scalable integrations between AI agents and external tools/APIs, leveraging open standards like the Model Context Protocol (MCP) to ensure interoperability
• Leverage AI-Powered Development: Utilize AI-powered development tools to accelerate the entire software development lifecycle from writing infrastructure code and tests to troubleshooting operational issues in cloud environments
• Infrastructure as Code (IaC): Utilize cloud-native IaC services or cloud-agnostic tools like Terraform to define and manage the infrastructure required for GenAI workloads
• Model Monitoring and Observability: Implement comprehensive monitoring and logging solutions to track model and agent performance, resource utilization and system health For agentic systems, this includes tracing the agent's actions and logging the multi-step conversational flow
• Scalability and Performance Optimization: Design and implement scalable architectures for model serving and inference Continuously optimize the performance and cost-effectiveness of our GenAI services
• Security and Compliance: Implement and enforce security best practices for our GenAI infrastructure and data Ensure compliance with industry standards and regulations

Requirements

• 3+ years in a DevOps, SRE or MLOps role with a focus on cloud infrastructure and a background in cloud services (AWS, GCP, Azure)
• Skilled in building and managing CI/CD pipelines (Jenkins, GitLab CI or cloud-native services) and proficiency in at least one scripting language (e.g. Python, Bash)
• Familiarity with IaC tools (e.g. AWS CDK, CloudFormation, Terraform) and in containerization and orchestration (Docker, Kubernetes)
• Track record deploying and operating LLM inference (e.g. vLLM, Triton, TGI, Ray Serve, KServe/Seldon)
• Hands-on with LLM/app tracing and metrics (e.g. OpenTelemetry + Langfuse, Arize Phoenix, WhyLabs) and building eval pipelines (offline/online regression suites)
• Skilled in operating retrieval pipelines: embedding generation, indexing/refresh strategies, vector DBs (Pinecone, Weaviate, Milvus, FAISS) and relevance monitoring
• Practice running multi-agent workflows (LangGraph, CrewAI, AutoGen-like), including state management, retries, rate limits, tool-failure handling and step-level auditing
• Experience in implementing guardrails: secrets isolation, tool/API permissions, prompt-injection defenses, data leakage prevention, PII redaction and policy enforcement

Nice to have

• Master's degree or PhD in Computer Science, AI, Machine Learning or a related field
• Background integrating agents with external tools using MCP (or similar tool-calling standards) and operating tool registries
• Experience with cloud-native GenAI services like AWS Bedrock, Azure AI Foundry or Google Vertex AI
• Familiarity with the architecture and operational challenges of Large Language Models (LLMs)
• Experience designing or managing multi-agent systems or complex orchestrated workflows
• Knowledge of monitoring and observability tools like Prometheus, Grafana or Datadog
• Relevant cloud or DevOps certifications
• Strong problem-solving skills and the ability to work effectively 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)

Please note that our Talent Attraction Team reviews applications and CVs submitted in English.

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.