At a Glance
- Tasks: Lead the development and deployment of cutting-edge AI systems for diverse clients.
- Company: Join Faculty, a leader in responsible AI with a focus on real-world impact.
- Benefits: Enjoy unlimited annual leave, private healthcare, and family-friendly flexibility.
- Why this job: Empower yourself to shape the future of AI in high-stakes environments.
- Qualifications: Experience in ML lifecycle, Python, and cloud platforms like AWS or Azure.
- Other info: Diverse team culture with opportunities for mentorship and growth.
The predicted salary is between 43200 - 72000 ÂŁ per year.
Why Faculty? We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we’ve worked with over 350 global customers to transform their performance through human‑centric AI. We don’t chase hype cycles. We innovate, build and deploy responsible AI which moves the needle - and we know a thing or two about doing it well. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence.
Our business, and reputation, is growing fast and we’re always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology. AI is an epoch‑defining technology, join a company where you’ll be empowered to envision its most powerful applications, and to make them happen.
About the team: Our Defence team is focused on building and embedding human‑centered AI solutions which give our nation a competitive edge in the defence sector. We collaborate with our clients to bring ethical, reliable and cutting‑edge AI to high‑stakes situations and maintain the balance of global powers essential to our liberty.
Because of the nature of the work we do with our Defence clients, you will need to be eligible for UK Security Clearance (SC) and willing to work between 2 to 4 days per week on‑site with these customers which may require travel to locations throughout the UK. When not required on client sites, you’ll have the flexibility to work from our London office or remotely from elsewhere within the UK.
About the role: As a Senior Machine Learning Ops Engineer, we’ll look to you to lead development and deployment of cutting‑edge AI systems for our diverse clients. You’ll design, build, and deploy scalable, production‑grade ML software and infrastructure that meets rigorous operational and ethical standards. This is an ambitious, cross‑functional role requiring a blend of technical expertise, engineering leadership, and confident client‑facing skills.
What you’ll be doing:
- Leading technical scoping and architectural decisions for high‑impact ML systems
- Designing and building production‑grade ML software, tools, and scalable infrastructure
- Defining and implementing best practices and standards for deploying machine learning at scale across the business
- Collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges and leverage opportunities
- Acting as a trusted technical advisor to customers and partners, translating complex concepts into actionable strategies
- Mentoring and developing junior engineers, actively shaping our team's engineering culture and technical depth
Who we’re looking for:
- You understand the full ML lifecycle and have significant experience operationalising models built with frameworks like TensorFlow or PyTorch
- You bring deep expertise in software engineering and strong Python skills, focusing on building robust, reusable systems
- You have demonstrable hands‑on experience with cloud platforms (e.g., AWS, Azure, GCP), including architecture, security, and infrastructure
- You’ve extensive experience working with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale
- You thrive in fast‑paced, high‑growth environments, demonstrating ownership and autonomy in driving projects to completion
- You communicate exceptionally well, confidently guiding both technical teams and senior, non‑technical stakeholders
The Interview Process:
- Talent Team Screen (30 minutes)
- Pair Programming Interview (90 minutes)
- System Design Interview (90 minutes)
- Commercial Interview (60 minutes)
Our Recruitment Ethos: We aim to grow the best team - not the most similar one. We know that diversity of individuals fosters diversity of thought, and that strengthens our principle of seeking truth. And we know from experience that diverse teams deliver better work, relevant to the world in which we live. We’re united by a deep intellectual curiosity and desire to use our abilities for measurable positive impact. We strongly encourage applications from people of all backgrounds, ethnicities, genders, religions and sexual orientations.
Some of our standout benefits:
- Unlimited Annual Leave Policy
- Private healthcare and dental
- Enhanced parental leave
- Family‑Friendly Flexibility & Flexible working
- Sanctus Coaching
- Hybrid Working (2 days in our Old Street office, London)
If you don’t feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please do apply or reach out to our Talent Acquisition team for a confidential chat. Please know we are open to conversations about part‑time roles or condensed hours.
Senior Machine Learning Ops Engineer in London employer: Faculty
Contact Detail:
Faculty Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Ops Engineer in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with Faculty employees on LinkedIn. Building relationships can open doors that applications alone can't.
✨Tip Number 2
Prepare for those interviews! Brush up on your technical skills and be ready to discuss your past projects. We want to see how you think and solve problems, so practice explaining your thought process clearly.
✨Tip Number 3
Show your passion for AI! When you get the chance, share your thoughts on the latest trends or ethical considerations in AI. This will demonstrate your intellectual curiosity and commitment to the field.
✨Tip Number 4
Don’t hesitate to apply through our website! Even if you don’t tick every box, we value enthusiasm and potential. If you’re excited about the role, we want to hear from you!
We think you need these skills to ace Senior Machine Learning Ops Engineer in London
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the Senior Machine Learning Ops Engineer role. Highlight your experience with ML frameworks like TensorFlow or PyTorch, and showcase how your skills align with our mission at Faculty.
Showcase Your Technical Skills: We want to see your technical prowess! Include specific examples of projects where you've designed and deployed scalable ML systems. Mention your experience with cloud platforms and container tools like Docker and Kubernetes to really stand out.
Communicate Clearly: Your ability to communicate complex ideas is key in this role. Use clear, concise language in your application to demonstrate how you can translate technical concepts into actionable strategies for clients and stakeholders.
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 your enthusiasm for joining our team!
How to prepare for a job interview at Faculty
✨Know Your ML Frameworks
Make sure you brush up on your knowledge of frameworks like TensorFlow and PyTorch. Be ready to discuss your hands-on experience with these tools, as well as how you've operationalised models in the past. This will show that you understand the full ML lifecycle and can hit the ground running.
✨Showcase Your Cloud Expertise
Familiarise yourself with cloud platforms such as AWS, Azure, or GCP. Be prepared to talk about your experience with architecture, security, and infrastructure. Highlight any projects where you've successfully deployed machine learning solutions in a cloud environment to demonstrate your practical skills.
✨Communicate Clearly
Since you'll be acting as a trusted technical advisor, practice explaining complex concepts in simple terms. Think about examples where you've guided both technical teams and non-technical stakeholders. Clear communication is key, especially when collaborating with diverse teams.
✨Prepare for Technical Challenges
Expect to face technical challenges during the pair programming and system design interviews. Brush up on your Python skills and container orchestration tools like Docker and Kubernetes. Practising coding problems and system design scenarios will help you feel more confident and ready to tackle these challenges.