At a Glance
- Tasks: Design and implement MLOps workflows using AWS SageMaker for machine learning platforms.
- Company: Join a leading tech firm in London focused on innovation and collaboration.
- Benefits: Competitive salary, flexible working 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 in the industry.
- 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 Engineering Specialist employer: 慨正橡扯
As an MLOps Engineering Specialist 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. 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 Engineering Specialist
✨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 using AWS SageMaker. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common MLOps scenarios. Think about how you would handle model monitoring or automated retraining strategies. Practising these will help you feel more confident when it’s your turn to shine.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like 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 Engineering Specialist
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the MLOps Engineering Specialist role. Highlight your hands-on experience with AWS and MLOps practices, and don’t forget to mention any relevant projects or achievements that showcase your skills.
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 your experience aligns with our needs. Be sure to mention specific tools and frameworks you’ve worked with, like SageMaker.
Showcase Your Problem-Solving Skills:In your application, give examples of how you've tackled complex problems in previous roles. We love candidates who can demonstrate their ability to deliver ambitious goals and navigate through challenges.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team!
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 that dive deep into your experience with model deployment and monitoring. Practice explaining your thought process when designing observability for serverless systems and how you handle drift detection.
✨Communicate Clearly and Confidently
Since excellent communication skills are a must, practice articulating your ideas clearly. Be prepared to explain complex concepts in simple terms, especially when discussing collaboration with data scientists.
