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We are looking for an accomplished Senior MLOps Engineer to join our forward-thinking Enterprise AI Products and Technology Team.
As an MLOps Engineer, you bring a software engineering mindset focused on automation, scalability, and improving data science workflows. You will work closely with data science teams to build tools, frameworks, and automation for the machine learning lifecycle, aligning platform capabilities with data scientists’ workflows.
Our team leads major AI initiatives such as clinical trial analysis, knowledge graph analytics, patient safety systems, and deep learning-driven medication discovery. This role offers an exciting opportunity to contribute to high-impact projects at the forefront of AI innovation in healthcare.
Responsibilities
- Collaborate with Data Scientists and Machine Learning Engineers from across the company to understand their challenges and work with them to build the tools/platforms that underpin their research
- Be a part of a high-performing agile team, continuously improving our client’s Machine Learning development environments, platforms and tooling to suit data science initiatives better
- Work closely and collaboratively with internal governance and compliance functions, such as Cyber Security and Data Privacy, to secure our estate without obstructing end-user productivity
- Adapt standard machine learning methods to exploit modern parallel environments best (e.g., distributed clusters, multicore SMP and GPU)
- Champion a "production first mindset" in the data science projects development lifecycle to seamlessly scale exploratory research to production
Requirements
- BSc/MSc/Ph.D degree in Computer Science or related quantitative or analytical field
- 3+ years of experience building and delivering software using the Python programming language (exceptional ability in other programming languages will be considered)
- Demonstrable experience in software engineering and automation leveraging DevOps
- Prior experience with developing and deploying production-grade machine learning products or exceptional ability in other software engineering domains will be considered
- In-depth knowledge and experience with at least one container orchestration framework (Airflow, Argo, Kubeflow, etc) or willingness to learn
- Demonstrable experience deploying the underlying infrastructure and tooling for running Machine Learning or Data Science at scale using Infrastructure as Code
- Experience working in an Agile team
- Experience working with internal security standards and frameworks
Benefits
- 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