• Turn machine learning engineer/scientist models into production services meeting latency, cost, and reliability targets
• Build and maintain SageMaker training, processing, and inference workloads
• Build pipelines that orchestrate SageMaker workloads
• Make training runs reproducible and configuration-driven so results can be rebuilt from code
• Build monitoring for model performance, data drift, and system health
• Ensure appropriate alerts are sent when model or system behaviour changes
• Ship infrastructure through code review using Terraform and pull-request workflows
• Document systems and raise engineering standards
• Own the platform and infrastructure that deploy and operate machine learning solutions across live hospitals
Requirements
- Expert Python
- Deep hands-on PyTorch experience; TensorFlow welcome
- Experience with Hugging Face Transformers, timm, scikit-learn, NumPy, pandas, and OpenCV
- Production experience on AWS, specifically Amazon SageMaker, including training and processing jobs, pipelines, model registry, and endpoints
- Practical MLOps experience with MLflow, hyperparameter optimisation, and reproducible, configuration-driven training runs
- Experience with Docker and CUDA-based GPU images
- Terraform experience sufficient to ship models through code review
- Strong software engineering fundamentals, including Git, pull-request workflow, automated testing, and CI/CD
- Ability to explain technical trade-offs clearly to non-engineers
- Desirable: event-driven and streaming architectures on AWS, including Step Functions, Lambda, Kinesis, ECS, and DynamoDB
- Desirable: model monitoring in production, inference logging, and drift detection
- Desirable: exposure to healthcare, medical devices, or another regulated industry
Core Competencies
Demonstrates expertise in building and maintaining machine learning production services, with a strong focus on AWS SageMaker, Python, and MLOps practices. Capable of ensuring model performance and system health through effective monitoring and alerting mechanisms.
Highest-signal resume keywords
- Expert Python
- AWS SageMaker
- MLOps Experience
- PyTorch
- Terraform
ATS Optimization Keywords
Hard Skills
- Python
- PyTorch
- TensorFlow
- MLflow
- Docker
- CUDA
- Git
- Automated Testing
- CI/CD
- Event-Driven Architectures
Soft Skills
- Clear Communication
Industry Keywords
- Healthcare
- Medical Devices
- Regulated Industry
Tools & Technologies
- SageMaker
- Hugging Face Transformers
- Scikit-learn
- NumPy
- Pandas
- OpenCV
- Step Functions
- Lambda
- Kinesis
- DynamoDB
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Senior MLOps Engineer employer: Jobtailor
As a Client Services Coordinator at our dynamic company, you will thrive in a supportive work culture that prioritises employee growth and development. We offer comprehensive training, opportunities for advancement, and a collaborative environment where your contributions are valued. Located in a vibrant area, our team enjoys a healthy work-life balance and the chance to engage with diverse clients, making every day rewarding and meaningful.
