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- Tasks: Transform ML models into production-ready services and ensure they run smoothly.
- Company: Global leader in behavioural intelligence technology with a focus on innovation.
- Benefits: Competitive salary, hybrid work model, and opportunities for professional growth.
- Why this job: Join a pivotal moment in tech and make a real impact on user authentication.
- Qualifications: 4+ years in MLOps or ML Engineering with strong Python skills.
- Other info: Dynamic role with high ownership and collaboration across teams.
The predicted salary is between 48000 - 72000 ÂŁ per year.
Helping Organisations Obtain Top Tech Talent
Our global client is building advanced behavioural intelligence technology that enables secure, adaptive digital identity. By analysing how people naturally interact with devices, their AI systems generate powerful authentication signals designed for real‑world use at scale. Our client is moving from R&D into live customer deployments and we’re looking for an experienced Senior MLOps Engineer to help take their behavioural AI models into production and keep them running reliably at scale. This is a hands‑on, high‑impact role at the intersection of machine learning and infrastructure. You’ll own how our models are trained, deployed, monitored, and scaled as real users start relying on them for authentication.
Responsibilities:
- Turning ML models into production‑ready, customer‑facing services
- Creating CI/CD pipelines for models, not just code
- Designing low‑latency, high‑availability inference infrastructure
- Monitoring live models for drift, performance drops, and failures
- Scaling ML systems as pilot customers onboard
- Working closely with AI, data, and software engineers to ship reliably
Qualifications:
- 4+ years in MLOps, ML Engineering, or ML‑heavy DevOps roles
- Strong Python and hands‑on ML framework experience (PyTorch, TensorFlow, etc.)
- Experience deploying and serving ML models in production
- Containerisation and orchestration (Docker, Kubernetes or ECS)
- CI/CD for ML workflows
Nice to Have:
- Model monitoring & observability (Prometheus, Grafana, Datadog)
- A/B testing or canary deployments for ML models
- Startup or scale‑up experience
- Work on real‑time behavioural AI used in authentication
- High ownership, you’ll shape how ML is run across the company for clients
- Direct impact as we move into live customer deployments
- Hybrid working (Manchester‑based)
- Join at a pivotal growth moment, not after everything is already decided
Seniority level: Mid‑Senior level
Employment type: Full‑time
Job function: Information Technology
Industries: Software Development
Senior Machine Learning Operations Engineer in Manchester employer: 55 Exec Search
Contact Detail:
55 Exec Search Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Operations Engineer in Manchester
✨Tip Number 1
Network like a pro! Reach out to folks in the MLOps community on LinkedIn or at local meetups. You never know who might have the inside scoop on job openings or can refer you directly to hiring managers.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your MLOps projects, especially those involving CI/CD pipelines and model deployment. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and ML frameworks. Practice coding challenges and be ready to discuss your past experiences with deploying and monitoring ML models.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you to join our team. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Senior Machine Learning Operations Engineer in Manchester
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in MLOps and ML Engineering. We want to see how your skills align with the role, so don’t be shy about showcasing your Python prowess and any hands-on work with ML frameworks like PyTorch or TensorFlow.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about the opportunity to work with behavioural AI and how your past experiences have prepared you for this high-impact role. Let us know what makes you tick!
Showcase Relevant Projects: If you've worked on any projects involving CI/CD pipelines for ML models or containerisation with Docker and Kubernetes, make sure to mention them. We love seeing real-world applications of your skills, so don’t hold back!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at 55 Exec Search
✨Know Your Tech Inside Out
Make sure you’re well-versed in the specific ML frameworks mentioned in the job description, like PyTorch and TensorFlow. Brush up on your Python skills too, as they’ll likely ask you to demonstrate your coding abilities during the interview.
✨Showcase Your MLOps Experience
Prepare to discuss your previous experience with CI/CD pipelines and how you've deployed ML models in production. Be ready to share specific examples of challenges you faced and how you overcame them, especially in a live customer environment.
✨Understand the Business Impact
Since this role is about turning ML models into customer-facing services, think about how your work can directly impact users. Be prepared to talk about how you’ve contributed to business goals in past roles, particularly in terms of reliability and performance.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about their current ML systems, deployment strategies, and how they measure success. This shows your genuine interest in the role and helps you gauge if it’s the right fit for you.
