Job Description: MLOps Engineering Specialist (AWS)

Role Overview

Job Title: MLOps Engineering Specialist (ARDEDC) 

Reports to: Senior Cloud Engineering Manager 

Team: Mobile DDOPs – Cloud & MLOps Engineering 

Location: London, UK Hours: 37.5 Career Level: D

Why BT Group?

BT Group was the world’s first telco and our heritage in the sector is unrivalled. As home to several of the UK’s most recognised and cherished brands – BT, EE, Openreach and Plusnet, we have always played a critical role in creating the future, and we have reached an inflection point in the transformation of our business.

Over the next two years, we will complete the UK’s largest and most successful digital infrastructure project – connecting more than 25 million premises to full fibre broadband. Together with our heavy investment in 5G, we play a central role in revolutionising how people connect with each other.

While we are through the most capital-intensive phase of our fibre investment, we are absolutely focused on how we organise ourselves in the best way to serve our customers in the years to come. This includes radical simplification of systems, structures, and processes on a huge scale. Together with our application of AI and technology, we are on a path to creating the UK’s best telco, reimagining the customer experience and relationship with one of this country’s biggest infrastructure companies.

Why This Job Matters

We are looking for an AWS MLOps Engineering Specialist to deploy and operate production-grade machine learning platforms using the AWS SageMaker MLOps framework. This role focuses on enabling the full ML lifecycle—data preparation, model deployment, monitoring, and retraining—through standardised, automated, and governed pipelines.

You will work at the intersection of data science, cloud engineering, and DevOps, ensuring models built in SageMaker can be reliably deployed at scale, monitored for drift and performance, and governed in line with enterprise and regulatory expectations. You will play a key role in standardising ML lifecycle practices, automating pipelines, and embedding operational excellence, security, and cost efficiency into AI/ML workloads.

What You’ll Be Doing – Your Accountabilities

•  Design and implement end-to-end MLOps workflows using AWS SageMaker, including:

• SageMaker Pipelines for training and orchestration

• SageMaker Feature Store for feature management

• SageMaker Model Registry for model versioning and approvals

• SageMaker Experiments for lineage and metadata tracking

• Enable consistent promotion of models across environments (dev / test / pre-prod / prod).

• Implement automated retraining strategies triggered by data or performance changes.

• Implement and mature an MLOps framework covering code/data/model versioning, automated testing, release governance, rollback strategies and environment promotion controls.

• Apply security-by-design across SageMaker workloads by adopting IAM least-privilege roles and ensuring Network isolation using VPC-attached SageMaker resources.

• Implement model monitoring (e.g. data quality, model quality, bias drift, feature attribution drift) and alerting driving automated responses.

• Put in place drift detection, evaluation routines, and model performance reporting; partner with data science to define thresholds and acceptance criteria.

• Define standards for documentation, change management and quality gates that reduce MTTR and improve platform reliability.

• Partner with data scientists to productionise notebooks and experiments into managed pipelines.

• Build scalable inference solutions using SageMaker real-time and serverless endpoints.

• Access, use, and disclose information only as required for the job; ensure adherence to Information Security policies.
  
  
  

The Skills You’ll Need to Succeed

•  Strong hands-on experience with MLOps practices: CI/CD, versioning (code/data/model), release governance, and production monitoring.
  Strong AWS experience, particularly with Amazon SageMaker for ML deployment and monitoring.

• Experience designing observability for serverless systems (logs/metrics/traces) and implementing distributed tracing/dashboards.
  
  

• Experience with supporting AWS services: S3, ECR, IAM, Lambda, Step Functions, Glue, and VPC networking.

• Containerisation experience (Docker) and familiarity with custom SageMaker containers.

• Infrastructure-as-Code (Terraform, CloudFormation, or CDK).

• Familiarity with monitoring, alerting, and incident response for ML platforms.

• Awareness of data privacy, model governance, and responsible AI considerations.

• Understanding of cost optimisation for training and inference workloads.

• Excellent verbal and written communication and interpersonal skills.
  
  
  

Experience You’d Be Expected to Have

• Degree in Computer Science/Engineering (or equivalent practical experience).

•  AWS Certifications strongly preferred (at least one):

• DevOps Engineer Professional

• Machine Learning Engineer – Associate

• AI Practitioner for GenAI fundamentals

• Knowledge of data governance, lineage, and model explainability practices.
  
    

Leadership Accountabilities

• Solution Focused Achiever: Deliver ambitious goals and cut through complexity to get to the right ethical solution.

• Change Agent: Identify and lead smooth business changes; adapt quickly even when there’s ambiguity.

• Team Coach: Coach and develop your people.

• Decision Making: Gather information, analyse scenarios, and reach decisions.
   
  
  

Being Trusted: Our Code

Compliance with all BT Group policies is mandatory. Policies can be accessed via the Policy Portal and should be adhered to in-line with Standards of Behaviour Policy & Procedure and the Being trusted: our code.