We are looking for an AI Platform Engineer to join our new Enterprise AI platform team.
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 theLife Sciences & Healthcare industry. You will bring proven experience in AWS cloud environments and a strong track record of designing and deploying large-scale production infrastructure and platforms.
You will play a critical role in shaping how we use technology, machine learning and data to accelerate innovation. This includes designing, building and deploying next-generation data engines and tools at scale.
#LI-DNI
Responsibilities
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- 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 work with them to 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, to enable the adoption of the latest advances in Data Processing and AI
- 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, however 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 Code Deploy
- 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 e.g., 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 on 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
- Why Join EPAM
- WORK AND LIFE BALANCE. Enjoy more of your personal time with flexible work options, 24 working days of annual leave and paid time off for numerous public holidays.
- CONTINUOUS LEARNING CULTURE. Craft your personal Career Development Plan to align with your learning objectives. Take advantage of internal training, mentorship, sponsored certifications and LinkedIn courses.
- CLEAR AND DIFFERENT CAREER PATHS. Grow in engineering or managerial direction to become a People Manager, in-depth technical specialist, Solution Architect, or Project/Delivery Manager.
- STRONG PROFESSIONAL COMMUNITY. Join a global EPAM community of highly skilled experts and connect with them to solve challenges, exchange ideas, share expertise and make friends.