ML Engineer

We are looking for a Machine Learning Engineer to join our team and contribute to the GenAI initiative. In this position, you will focus on creating, enhancing, and fine-tuning backend systems that drive LLM-powered applications utilizing OpenAI APIs. Your expertise in MLOps, CI/CD, observability, and cloud-native tools will be critical in ensuring the performance, reliability, and scalability of AI-driven solutions.

Responsibilities

• Build and enhance backend systems for AI and LLM-powered applications
• Integrate LLM applications into cloud platforms and manage their operations
• Scale AI systems to meet performance and reliability goals
• Create CI/CD pipelines to enable automated deployment processes
• Monitor the performance of AI services to ensure system stability
• Set up observability and logging to track the performance of LLM APIs
• Work with DevOps teams to optimize workflows and improve system reliability
• Collaborate with AI and Data Science teams to expand and refine application features
• Utilize cloud platforms, particularly Azure, for hosting and scaling AI applications
• Design APIs and microservices architecture to enable AI functionalities

Requirements

• A minimum of 2 years of experience in Machine Learning Engineering with a focus on backend and software systems
• Extensive experience in integrating OpenAI APIs and AI services
• Proficiency with MLOps tools such as Orion, ArgoCD, and Opsera for automation of deployments
• Experience using monitoring and observability platforms like Grafana, Dynatrace, or ThoughtSpot
• Strong knowledge of cloud infrastructure, with a preference for Azure, as well as expertise in Apache Spark and Databricks
• Advanced Python programming skills for backend development
• Proven experience in developing APIs and designing microservices architectures
• Fluency in English, both written and spoken, with a proficiency level of B2+ or higher

Nice to have

• Understanding of Data Science concepts and methodologies
• Experience working with Large Language Models (LLMs)
• Familiarity with Natural Language Processing (NLP) techniques and 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