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- Tasks: Transform machine learning models into production-ready setups and improve existing systems.
- Company: Dynamic tech company focused on innovative machine learning solutions.
- Benefits: Remote work, competitive salary, and opportunities for professional growth.
- Other info: Join a supportive team with a focus on practical solutions and career development.
- Why this job: Make a real impact by enhancing ML processes and collaborating with talented teams.
- Qualifications: Experience in ML lifecycle management and strong Python skills required.
The predicted salary is between 60000 - 80000 £ per year.
Location: Remote (occasional travel if needed to London HO)
Start Date: ASAP
We’re looking for an experienced ML Engineer / MLOps Consultant to help a business move from early‑stage machine learning into a more structured, production‑ready setup. You’ll work closely with a data scientist and engineering team to design and implement a clean, maintainable approach to model training, deployment, and monitoring.
The business already has models in production and a basic SageMaker setup in place, but it’s currently clunky and not scalable long‑term. This role is about assessing the current environment, improving or simplifying it, and putting the right foundations in place so models can be reliably built, deployed, and maintained going forward. They need someone to support them with a pragmatic approach whilst being hands‑on and engaged with the team’s work.
Key Experience Needed
- Proven experience taking ML models from notebook / experimentation into production environments
- Strong understanding of ML lifecycle management (training, deployment, monitoring, retraining)
- Experience with AWS (ideally SageMaker, but not essential)
- Experience building and managing model APIs / model serving infrastructure
- Strong Python skills and experience working with software engineering best practices
- Experience working in small teams or consultative environments
- Ability to design simple, pragmatic solutions rather than overengineered systems
- Strong communication skills, with experience supporting or upskilling data scientists or engineers
Key Responsibilities
- Assess and improve (or replace) the current SageMaker-based ML setup
- Put models behind reliable APIs for production use
- Establish best practices for versioning, retraining, and performance monitoring
- Work closely with the data scientist to enable greater ownership of models in production
- Bridge the gap between data science and software engineering teams
- Introduce structure and standards to how ML is developed and deployed
- Document processes and approaches to support future model development
MLOps Consultant in London employer: Understanding Solutions
Contact Detail:
Understanding Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land MLOps Consultant in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the ML and MLOps space. Attend meetups, webinars, or even online forums. You never know who might have a lead on that perfect role!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving model deployment and monitoring. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge. Be ready to discuss your experience with AWS and how you've tackled challenges in previous roles. Confidence is key!
✨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 the attention it deserves.
We think you need these skills to ace MLOps Consultant in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with ML models and production environments. We want to see how you've taken models from experimentation to deployment, so don’t hold back on those details!
Showcase Your Skills: When writing your cover letter, emphasise your strong Python skills and any experience with AWS or SageMaker. We’re looking for someone who can bridge the gap between data science and software engineering, so let us know how you’ve done that in the past.
Be Pragmatic: In your application, share examples of how you've designed simple, effective solutions rather than overcomplicated systems. We appreciate a hands-on approach, so if you’ve improved existing setups, tell us about it!
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at Understanding Solutions
✨Know Your ML Lifecycle
Make sure you can confidently discuss the entire machine learning lifecycle, from training to deployment and monitoring. Be prepared to share specific examples of how you've taken models from experimentation to production, as this will show your hands-on experience.
✨Showcase Your AWS Skills
Even if you haven't worked extensively with SageMaker, brush up on your AWS knowledge. Familiarise yourself with its features and be ready to discuss how you would improve or simplify an existing setup. This will demonstrate your proactive approach and understanding of cloud environments.
✨Communicate Clearly
Strong communication skills are key in this role. Practice explaining complex concepts in simple terms, especially how you would bridge the gap between data science and software engineering teams. This will highlight your ability to support and upskill others effectively.
✨Prepare for Pragmatic Problem-Solving
Expect questions that assess your ability to design simple, pragmatic solutions. Think of past challenges you've faced and how you approached them without overengineering. This will showcase your practical mindset and ability to deliver results in a consultative environment.