Senior MLOps Engineer

Jobtailor; Greater London; On-site; GBP 85,000 - 120,000; Full time

Job summary

Jobtailor is seeking a senior ML Engineer/Scientist to turn ML models into production services with latency and reliability targets. You will own SageMaker training, processing and inference workloads and build end-to-end pipelines.

You will implement reproducible training runs, monitor model performance and data drift, and ship infrastructure via Terraform and PR workflows. Experience in healthcare tech is a plus.

Qualifications

• Expert Python
• Deep hands-on PyTorch; TensorFlow welcome
• Experience with Hugging Face Transformers, timm, scikit-learn, NumPy, pandas, and OpenCV
• Production experience on AWS, SageMaker including training/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 (Step Functions, Lambda, Kinesis, ECS, DynamoDB)
• Desirable: model monitoring in production, inference logging, and drift detection
• Desirable: exposure to healthcare, medical devices, or regulated industry

Responsibilities

• Turn ML 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 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 ML solutions across live hospitals

Skills

Python

PyTorch

TensorFlow

SageMaker

MLOps

Docker

Git

CI/CD

MLflow

Transformers

Tools

Terraform

Kubernetes

Job description

• 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