Back to SearchSenior Machine Learning Engineer
Hybrid in Ukraine
Machine Learning Engineering
& 6 others
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
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