At a Glance
- Tasks: Own the reliability and scalability of ML systems, designing data pipelines and tooling.
- Company: Join a forward-thinking tech company focused on machine learning innovation.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on cutting-edge technology and career advancement.
- Why this job: Make a real impact by ensuring ML systems operate reliably at scale.
- Qualifications: 3-7 years in ML Ops or Data Engineering with strong Python skills.
The predicted salary is between 60000 - 80000 € per year.
Requirements
- 3–7 years of professional experience in ML Ops, Data Engineering, or adjacent backend roles
- Strong production Python skills (clean APIs, testing, performance awareness)
- Experience deploying and operating ML models in production environments
- Solid understanding of:
- Model training vs. inference requirements
- Batch vs. streaming data pipelines
- Failure modes in data-driven systems
- Hands-on experience with at least one modern orchestration or workflow system
- Comfort working with cloud infrastructure and containerized workloads
- Ability to reason about system design, not just tool usage
- (Desirable) Experience operating systems at TB-scale data volumes or higher
- (Desirable) Prior ownership of model monitoring, drift detection, or automated retraining
- (Desirable) Familiarity with feature stores or online/offline feature consistency problems
- (Desirable) Experience supporting multiple models or teams on a shared ML platform
- (Desirable) Exposure to regulated or high-reliability production environments
What the job involves
We’re hiring an ML Ops Engineer to own the reliability, scalability, and operational integrity of our machine-learning systems in research & production. This role sits at the intersection of data engineering and ML infrastructure: you’ll design and operate data pipelines that feed models, and you’ll build the tooling that trains, deploys, monitors, and retrains them. You’ll work closely with research engineers and product teams, taking models from experimentation to production-grade systems with clear SLAs, reproducibility guarantees, and observable behaviour. This is not a research role; it is a hands-on engineering role focused on making ML systems work reliably at scale.
Key Responsibilities
- Productionizing models: packaging, deployment, versioning, and rollback
- Designing CI/CD pipelines for ML (training → validation → deployment)
- Implementing model monitoring (data drift, prediction drift, performance decay)
- Managing experiment tracking and reproducibility
- Building and maintaining batch and near-real-time data pipelines
- Ensuring data quality, schema evolution, and lineage across systems
- Designing datasets and feature pipelines that support both training and inference
- Operating pipelines with clear reliability and latency expectations
- Defining and meeting availability, latency, and freshness targets for ML services
- Debugging production issues across data, infrastructure, and model layers
- Improving system robustness through automation and observability
- Collaborating with platform and security teams on access, secrets, and compliance
- Writing production-grade Python used in long-running services and pipelines
- Establishing testing, validation, and release practices for ML systems
- Making trade-offs explicit between research flexibility and production stability
ML Ops Engineer in London employer: Deepstreamtech
As an ML Ops Engineer at our company, you will thrive in a dynamic and innovative work culture that prioritises collaboration and continuous learning. We offer competitive benefits, including professional development opportunities and a supportive environment that encourages growth and creativity. Located in a vibrant tech hub, you'll have access to cutting-edge resources and a network of like-minded professionals, making this an ideal place for those seeking meaningful and rewarding employment in the field of machine learning.
StudySmarter Expert Advice🤫
We think this is how you could land ML Ops Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the ML Ops community on LinkedIn or at 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 projects, especially those involving model deployment and data pipelines. This gives potential employers a taste of what you can do beyond your CV.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python skills and understanding ML systems. Practice explaining your past projects and how you tackled challenges in production environments.
✨Tip Number 4
Don’t forget to apply through our website! We love seeing candidates who are genuinely interested in joining us at StudySmarter. Tailor your application to highlight your experience with ML Ops and data engineering.
We think you need these skills to ace ML Ops Engineer in London
Some tips for your application 🫡
Show Off Your Experience:Make sure to highlight your 3–7 years of experience in ML Ops or related fields. We want to see how you've tackled real-world challenges, so share specific examples of your work with production Python and deploying ML models.
Be Clear and Concise:When writing your application, keep it straightforward. We appreciate clarity, so avoid jargon and get straight to the point about your skills and experiences that align with the job description.
Tailor Your Application:Don’t just send a generic application! Tailor your CV and cover letter to reflect the specific requirements of the ML Ops Engineer role. Mention your hands-on experience with orchestration systems and cloud infrastructure to catch our eye.
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 Deepstreamtech
✨Know Your Stuff
Make sure you brush up on your ML Ops and data engineering knowledge. Be ready to discuss your experience with deploying and operating ML models, as well as your understanding of batch vs. streaming data pipelines. They’ll want to hear about specific projects where you’ve tackled these challenges.
✨Showcase Your Python Skills
Since strong production Python skills are a must, prepare to demonstrate your ability to write clean APIs and perform testing. You might be asked to solve a coding problem or explain how you ensure performance awareness in your code, so practice articulating your thought process.
✨Understand System Design
This role requires a solid grasp of system design beyond just tool usage. Be prepared to discuss how you would approach designing CI/CD pipelines for ML, managing experiment tracking, and ensuring data quality. Think through potential failure modes and how you would address them.
✨Be Ready for Real-World Scenarios
Expect questions that dive into your hands-on experience with orchestration systems and cloud infrastructure. They may ask about your experience with model monitoring, drift detection, or handling high-reliability production environments. Have examples ready that highlight your problem-solving skills in these areas.
