MLOps Engineering Specialist

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

We are looking for an AWS MLOps Engineering Specialist to deploy and operate production‑grade machine learning platforms using the AWS Sage Maker 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 Dev Ops, ensuring models built in Sage Maker 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.

• Design and implement end‑to‑end MLOps workflows using AWS Sage Maker, including:
  • Sage Maker Pipelines for training and orchestration
  • Sage Maker Feature Store for feature management
  • Sage Maker Model Registry for model versioning and approvals
  • Sage Maker 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 Sage Maker workloads by adopting IAM least‑privilege roles and ensuring network isolation using VPC‑attached Sage Maker resources.
• Implement model monitoring—including 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 product ionise notebooks and experiments into managed pipelines.
• Build scalable inference solutions using Sage Maker real‑time and serverless endpoints.
• Access, use, and disclose information only as required for the job; ensure adherence to Information Security policies.

• Strong hands‑on experience with MLOps practices: CI/CD, versioning (code/data/model), release governance, and production monitoring.
• Strong AWS experience, particularly with Amazon Sage Maker for ML deployment and monitoring.
• Experience designing observability for serverless systems (logs/metrics/traces) and implementing distributed tracing and dashboards.
• Experience with supporting AWS services: S3, ECR, IAM, Lambda, Step Functions, Glue, and VPC networking.
• Containerisation experience (Docker) and familiarity with custom Sage Maker containers.
• Infrastructure‑as‑Code (Terraform, Cloud Formation, 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.

• Degree in Computer Science/Engineering (or equivalent practical experience).
• AWS Certifications strongly preferred (at least one):
  • Dev Ops Engineer Professional
  • Machine Learning Engineer – Associate
  • AI Practitioner for GenAI fundamentals
• Knowledge of data governance, lineage, and model explainability practices.

• 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.