mlops engineer for generative AI

EPAM develops enterprise software products, open source solutions, and technology accelerators.

• Implement and maintain CI/CD pipelines for AI and machine learning projects, ensuring robust deployment strategies and continuous integration;
• Monitor and ensure the reliability, availability, and performance of AI applications, particularly those involving LLMs and RAG;
• Collaborate with AI research teams to operationalize machine learning models and systems efficiently;
• Develop and enforce best practices for version control, configuration management, and testing of AI-driven software solutions;
• Use MLOps tools such as Kubeflow, MLflow, or TensorFlow Extended (TFX) to streamline the machine learning lifecycle from experimentation to production;
• Implement monitoring solutions that track system metrics and model performance to facilitate proactive issue resolution;
• Participate in on-call rotations to support the operational health of critical systems, applying SRE principles to meet service-level objectives (SLOs) and reduce downtime.

• Bachelor’s degree in Computer Science, Engineering, or a related field;
• Proven experience as a DevOps Engineer or SRE, with a strong background in software development and automation;
• Strong familiarity with Generative AI Operations;
• Expertise in deploying and managing LLMs, including technologies such as RAG;
• Proficiency with CI/CD tools, including Jenkins, GitLab CI, and CircleCI;
• Proficiency with infrastructure as code using Terraform and Ansible;
• Solid knowledge of container orchestration technologies, including Kubernetes and Docker;
• Familiarity with MLOps tools and practices supporting machine learning lifecycle management;
• Nice to have: Experience with AWS, GCP, or Azure for AI/ML deployments, Prometheus, Grafana, and ELK stack, Python in data science and machine learning contexts, certification in Kubernetes, AWS/GCP/Azure, or similar technologies.

• No conditions specified