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- Tasks: Transform machine learning models into production-ready setups and improve existing systems.
- Company: Dynamic tech company focused on innovative ML solutions.
- Benefits: Remote work, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with potential for career advancement.
- Why this job: Join a team making a real impact in the ML space with hands-on experience.
- 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 employer: Understanding Solutions
Contact Detail:
Understanding Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land MLOps Consultant
✨Tip Number 1
Network like a pro! Reach out to your connections in the ML and MLOps space. Attend meetups, webinars, or even local tech events. 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 projects, especially those involving model deployment and monitoring. Share it on platforms like GitHub or your personal website. This gives potential employers a taste of what you can do!
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge. Be ready to discuss your experience with AWS, SageMaker, and Python. Practice explaining complex concepts in simple terms – it shows you can bridge the gap between data science and engineering.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Tailor your application to highlight your hands-on experience and pragmatic approach to MLOps – it’ll make you stand out!
We think you need these skills to ace MLOps Consultant
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 application, 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 cover letter, share examples of how you've designed simple, effective solutions rather than overcomplicated systems. We appreciate a hands-on approach, so tell us about your practical experiences!
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 this exciting opportunity as an MLOps Consultant!
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 Pragmatic Approach
During the interview, highlight your ability to design simple, effective solutions rather than overcomplicated systems. Share instances where you've improved existing setups, like SageMaker, and explain how your changes made a tangible difference.
✨Communicate Effectively
Strong communication skills are key in this role. Be ready to discuss how you've supported or upskilled data scientists or engineers in the past. Use clear examples to demonstrate your ability to bridge the gap between data science and software engineering.
✨Prepare for Technical Questions
Brush up on your Python skills and be ready to answer technical questions related to model APIs and serving infrastructure. Familiarise yourself with AWS services, especially SageMaker, and be prepared to discuss best practices for versioning and performance monitoring.