At a Glance
- Tasks: Design and implement MLOps workflows using AWS SageMaker for machine learning platforms.
- Company: Join a leading tech firm focused on cloud and MLOps engineering.
- Benefits: Competitive salary, flexible hours, and opportunities for professional growth.
- Other info: Dynamic team environment with excellent career advancement opportunities.
- Why this job: Be at the forefront of AI/ML technology and make a real impact.
- Qualifications: Experience with MLOps practices and strong AWS skills required.
The predicted salary is between 60000 - 80000 £ per year.
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—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 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 and 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.
MLOps Engineer — AWS SageMaker Platform Architect employer: 慨正橡扯
As an MLOps Engineer at our London office, you will join a dynamic team that values innovation and collaboration, providing you with the opportunity to work on cutting-edge machine learning platforms using AWS SageMaker. We offer a supportive work culture that prioritises employee growth through continuous learning and development, alongside competitive benefits that enhance work-life balance. Our commitment to operational excellence and security ensures that you will be part of a forward-thinking environment where your contributions directly impact the success of our AI/ML initiatives.
StudySmarter Expert Advice🤫
We think this is how you could land MLOps Engineer — AWS SageMaker Platform Architect
✨Tip Number 1
Network like a pro! Reach out to folks in the MLOps community on LinkedIn or attend local meetups. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your MLOps projects, especially those involving AWS SageMaker. This gives potential employers a tangible look at what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common MLOps scenarios. Be ready to discuss how you've implemented CI/CD pipelines or handled model monitoring. Practice makes perfect, so consider mock interviews with friends or mentors.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might be just right for you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace MLOps Engineer — AWS SageMaker Platform Architect
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the MLOps Engineer role. Highlight your experience with AWS SageMaker and any relevant MLOps practices. We want to see how your skills align with what we’re looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about MLOps and how you can contribute to our team. Let us know what excites you about working with AWS and machine learning.
Showcase Your Projects:If you’ve worked on any projects related to MLOps, make sure to mention them! Whether it’s deploying models or automating pipelines, we love seeing real-world examples of your work and how you’ve tackled challenges.
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at 慨正橡扯
✨Know Your MLOps Inside Out
Make sure you brush up on your MLOps practices, especially around CI/CD, versioning, and monitoring. Be ready to discuss how you've implemented these in past projects, particularly with AWS SageMaker.
✨Showcase Your AWS Expertise
Familiarise yourself with all the AWS services mentioned in the job description, like S3, ECR, and Lambda. Prepare examples of how you've used these services to build scalable ML solutions.
✨Prepare for Technical Questions
Expect technical questions about designing observability for serverless systems and implementing distributed tracing. Practise explaining your thought process clearly and concisely.
✨Communicate Effectively
Since excellent communication skills are a must, practise articulating complex concepts in simple terms. Think about how you can explain your past experiences and technical knowledge in a way that’s easy to understand.
