Reproduce training environments with pinned dependencies and seeds, rebuild feature and label pipelines, and register models
Deploy SageMaker endpoints and batch jobs with autoscaling, execute shadow/canary strategies, and define rollback protocols
Set up ML experiments, Model Registry, and Feature Stores, integrating them into CI/CD workflows
Implement Model Monitor for drift and performance tracking, configure alerting mechanisms, and create comprehensive runbooks
Optimize pipelines for reliable model serving, ensuring low latency, high quality, and rollback readiness during hypercare
Collaborate with data engineers to build scalable data pipelines on S3/Snowflake for training and inference workflows

Requirements

3+ years of hands-on experience in machine learning engineering, with a solid background in Python 3.x
Proficiency in PyTorch or TensorFlow, with a track record of building and deploying models in production
Expertise in AWS SageMaker, including Pipelines, Registry, Endpoints, and Model Monitor
Familiarity with MLflow or SageMaker Experiments for experiment tracking and artifact management
Strong skills in Docker for containerization of ML workloads
Experience with Feature Store solutions such as SageMaker Feature Store or Feast
English level B1+ for effective communication

Nice to have

Proficiency in FastAPI or BentoML for lightweight API service development
Knowledge of ONNX for model optimization and cross-platform interoperability
Familiarity with distributed inference tools like Ray Serve or Triton
Experience with large-scale distributed training using Horovod or DeepSpeed
Understanding of Hugging Face Transformers for state-of-the-art NLP model implementation

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

Machine Learning Engineering & 6 others