Design, build and maintain ML Ops pipelines that support model training, validation and deployment across AWS environments
Implement automation for model packaging, testing, deployment and monitoring using CI/CD best practices
Collaborate with data engineers and data scientists to operationalize ML workloads within the data Lakehouse ecosystem
Develop and maintain integrations between data ingestion, feature stores and model repositories
Apply infrastructure-as-code such as Terraform, AWS CDK or CloudFormation to automate ML pipeline infrastructure
Implement and manage model versioning, reproducibility and lineage tracking using tools such as MLflow or SageMaker Model Registry
Define and automate monitoring, alerting and retraining strategies for deployed models
Ensure all ML infrastructure and pipelines meet enterprise security, compliance and governance standards
Participate in code reviews, knowledge sharing and continuous improvement of ML Ops practices
Mentor junior engineers and contribute to documentation, standards and best practices for ML Ops across teams

Requirements

Bachelor’s Degree in Computer Science, Data Engineering or a related technical field or equivalent experience
6+ years professional experience in software, data or ML engineering
3+ years direct experience implementing and maintaining ML pipelines in production
Proficiency in Python and familiarity with ML frameworks such as PyTorch, TensorFlow or Scikit-learn
Skills in AWS services such as SageMaker, Step Functions, Lambda, ECR, S3, Glue and IAM
Understanding of CI/CD and containerization such as Docker
Skills in building CI/CD pipelines such as Jenkins or Github Actions
Skills in infrastructure-as-code and automation such as Terraform, AWS CDK or CloudFormation
Understanding of data pipelines, ETL/ELT concepts and feature engineering in a Lakehouse environment
Capability to apply software engineering practices to machine learning workflows
Communication and collaboration skills across multidisciplinary teams

We offer/Benefits

With us you can:

Work on a flexible schedule remotely or from any of our comfortable offices or coworking spaces in Ukraine
Receive the necessary equipment to perform your work tasks
Change projects and technology stacks within EPAM
Gain experience in various business domains (Insurance, E-commerce, Healthcare, Finance, Travelling, Media, Artificial Intelligence, and more)
Relocation opportunities may be available for eligible candidates, depending on the role and openings at other EPAM locations
Participate in volunteer, charity programs and communities (both technical and interest-based)

We focus on your professional growth:

You can plan your individual career path together with your manager
Receive regular feedback from colleagues
Improve your English for free with certified teachers (Speaking Clubs, client interview preparation courses, etc.)
Get the opportunity to undergo free training and certification in AWS, GCP, or Azure Clouds
Use the internal E-learn training program (18,200+ specialized training and mentoring programs)
Access corporate accounts on LinkedIn Learning, Get Abstract and other partner resources
Study at EPAM Solution Architecture School with the instructors who are practicing architects
Develop as a leader, join Delivery Management, Resource Management, Leadership Essentials school and more
Participate in internal communities (500+ meetups, technical discussions, brainstorming sessions, online events and conferences annually)

What we offer:

Vacation and sick leave (including a sick leave without a medical certificate)
A wide range of Voluntary Medical Insurance programs providing both medical treatment and various preventive options (including sports activities)
Medical insurance for family members at corporate rates
Company support during significant life events (childbirth or adoption, marriage, etc.)
Support for psychological comfort: discounts on services from mental health specialists or coaches, thematic training
E-kids program - a free programming language training program for EPAMers' children

Data Solution Architecture