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Senior Machine Learning Engineer
- Pay
- Pay not listed
- Work setting
Remote — work setting source
Work setup in the employer’s posting: TELECOMMUTE
From the employer’s posting- Employment
- Not stated
What you’ll work on
Full postingDeploy SageMaker endpoints and batch jobs with autoscaling and canary rollouts
Reproduce training environments and rebuild feature and label pipelines
Set up experiments, Model Registry and Feature Stores within CI/CD workflows
Implement Model Monitor for drift and performance, with alerts and runbooks
Build scalable S3/Snowflake data pipelines with data engineers
From the employer’s posting
Deploy SageMaker endpoints and batch jobs with autoscaling, execute shadow/canary strategies, and define rollback protocols
Reproduce training environments with pinned dependencies and seeds, rebuild feature and label pipelines, and register models
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
Collaborate with data engineers to build scalable data pipelines on S3/Snowflake for training and inference workflows
Posting record
Report an errorActively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 1, 20262 days ago
- Recorded sightings
- 4
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Sep 29, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
EPAM Systems is hiring
656 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- AWS
- Docker
- MLflow
- S3
- SageMaker
- Snowflake
- FastAPI
- Hugging Face
- PyTorch
- TensorFlow
- Transformers
Where the posting names them
We are seeking a highly skilled and experienced Senior Machine Learning Engineer to drive the development, deployment, and monitoring of cutting-edge machine learning solutions in a scalable cloud environment. You will collaborate with a cross-functional team to deliver end-to-end ML pipelines, ensure high-quality model performance, and implement robust monitoring for production systems. Responsibilities 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
Your next step
Complete your application on careers.epam.com. The employer’s form will show what is required.
Already applied? Track this application
Has EPAM Systems posted this job before?
Yes. We’ve followed 6 other Senior Machine Learning Engineer jobs there since November 2026. All 6 are still up.
Large employers often post one title for several teams, so these can be separate openings.
Job description
We are seeking a highly skilled and experienced Senior Machine Learning Engineer to drive the development, deployment, and monitoring of cutting-edge machine learning solutions in a scalable cloud environment. You will collaborate with a cross-functional team to deliver end-to-end ML pipelines, ensure high-quality model performance, and implement robust monitoring for production systems. Responsibilities 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
Source details
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