DevOps Engineer

• Develop and maintain the essential infrastructure and platform required to deploy, monitor and manage ML solutions in production, ensuring they are optimized for performance and scalability.
• Collaborate closely with data science teams in developing cutting-edge data science, AI/ML environments and workflows on AWS.
• Liaise with R&D data scientists to understand their challenges and help productionize ML pipelines, models and algorithms for innovative science.
• Take responsibility for all aspects of software engineering, from design to implementation, QA and maintenance.
• Lead technology processes from concept development to completion of project deliverables.
• Liaise with other teams to enhance our technological stack and enable the adoption of the latest advances in Data Processing and AI.

This is an exciting opportunity to be part of a high-impact, highly technical group focused on solving some of the most challenging machine learning problems in Life Sciences & Healthcare. You will bring proven experience in AWS cloud environments and a strong track record of designing and deploying large-scale production infrastructure and platforms., * Significant experience with AWS cloud environments is essential. Knowledge of SageMaker, Athena, S3, EC2, RDS, Glue, Lambda, Step Functions, EKS and ECS is also essential.

• Modern DevOps mindset, using best DevOps tools such as Docker and Git.
• Experience with infrastructure as code technology such as Ansible, Terraform and Cloud Formation.
• Strong software coding skills, with proficiency in Python; exceptional ability in any language will be recognized.
• Experience managing an enterprise platform and service, handling new client demand and feature requests.
• Experience with containers and microservice architectures (e.g., Kubernetes, Docker, and serverless approaches).
• Experience with Continuous Integration and building continuous delivery pipelines, such as CodePipeline, CodeBuild and CodeDeploy.
• GxP experience.
• Excellent communication, analytical and problem-solving skills., * Experience building large-scale data processing pipelines (e.g., Hadoop/Spark and SQL).
• Use of Data Science modelling tools such as R, Python and Data Science notebooks (e.g., Jupyter).
• Multi-cloud experience (AWS/Azure/GCP).
• Demonstrable knowledge of building MLOps environments to a production standard.
• Experience mentoring, coaching and supporting less experienced colleagues and clients.
• Experience with SAFe agile principles and practices.

• Private health insurance.
• EPAM Employees Stock Purchase Plan.
• 100% paid sick leave.
• Referral Program.
• Professional certification.
• Language courses.

full_time

Organization EPAM Systems