Lead Generative AI Operations Engineer

We are seeking a Lead Generative AI Operations Engineer to architect and sustain a robust ML infrastructure that supports seamless AI deployment.

In this role, you will work cross-functionally to develop scalable MLOps pipelines and infrastructure, enabling data scientists and engineers to transition ML projects from prototype stages to production environments. Join us to make a significant impact on AI services within the IT Chief Data Office.

Responsibilities

• Design scalable AI and machine learning workloads that align with company objectives
• Develop and uphold reproducible machine learning pipelines
• Deploy AI models into production using model serving infrastructures
• Implement monitoring and logging frameworks for AI service observability
• Define infrastructure needs for MLOps pipelines and related components
• Collaborate with infrastructure engineers to facilitate infrastructure deployment
• Guide and mentor team members to encourage best practices and ongoing improvement
• Coordinate efforts with cross-functional teams including data scientists and engineers
• Optimize machine learning workloads for enhanced performance and scalability
• Ensure adherence to security protocols and data privacy regulations
• Assess new tools and technologies to improve AI service delivery
• Document system designs and workflows for knowledge dissemination
• Diagnose and resolve production issues affecting AI services

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related discipline
• Over 5 years of experience in AI, machine learning, data engineering, software development, or cloud infrastructure
• Strong expertise in Python and proficiency with AI/ML frameworks such as PyTorch, TensorFlow, HuggingFace, or Scikit-learn
• Experience with model inference runtimes including vLLM, MLServe, or Torch Serve
• Proficiency in containerization and orchestration technologies such as Docker and Kubernetes
• Experience specifying and implementing infrastructure requirements for ML pipelines
• Strong analytical and problem-solving capabilities with experience in agile cross-disciplinary teams
• Effective communication and mentoring abilities to support team growth
• English language proficiency at B2 level or higher

Nice to have

• Familiarity with cloud platforms like Azure, AWS, or Google Cloud
• Understanding of Infrastructure as Code (IaC) methodologies
• Experience with experiment tracking systems and pipeline orchestration tools

We offer

• International projects with top brands
• Work with global teams of highly skilled, diverse peers
• Healthcare benefits
• 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