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 innovation since 2014.
- Benefits: Enjoy unlimited annual leave, private healthcare, and flexible working options.
- Why this job: Make a real impact with AI in high-stakes situations while shaping the future.
- Qualifications: Experience in ML lifecycle, Python, cloud platforms, and container tools required.
- Other info: Diverse team culture with excellent career growth and mentorship opportunities.
The predicted salary is between 48000 - 84000 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 - talent@faculty.ai. Please know we are open to conversations about part‑time roles or condensed hours.
Senior Machine Learning Ops Engineer employer: Faculty
Contact Detail:
Faculty Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Ops Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with Faculty employees on LinkedIn. A personal introduction can make all the difference when it comes to landing that interview.
✨Tip Number 2
Prepare for those technical interviews! Brush up on your ML lifecycle knowledge and be ready to discuss your experience with frameworks like TensorFlow or PyTorch. Practising coding challenges can also help you shine during the pair programming interview.
✨Tip Number 3
Showcase your soft skills! Faculty values communication, so be prepared to demonstrate how you can translate complex concepts into actionable strategies. Think of examples where you've successfully guided teams or clients through technical challenges.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at Faculty.
We think you need these skills to ace Senior Machine Learning Ops Engineer
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter to highlight your experience with ML systems and cloud platforms. We want to see how your skills align with the role, so don’t hold back on showcasing your relevant projects!
Showcase Your Technical Skills: When detailing your experience, focus on your expertise in frameworks like TensorFlow or PyTorch, and your hands-on work with Docker and Kubernetes. We love seeing specific examples of how you've tackled challenges in previous roles.
Be Clear and Concise: Keep your application straightforward and to the point. Use clear language to explain complex concepts, as we value communication skills just as much as technical prowess. Remember, we’re looking for someone who can bridge the gap between tech and non-tech stakeholders!
Apply Through Our Website: We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it shows you’re keen to join our team at Faculty!
How to prepare for a job interview at Faculty
✨Know Your ML Lifecycle
Make sure you can confidently discuss the full machine learning lifecycle. Brush up on your experience with frameworks like TensorFlow and PyTorch, and be ready to share specific examples of how you've operationalised models in past projects.
✨Showcase Your Technical Skills
Prepare to demonstrate your software engineering prowess, especially in Python. Be ready to talk about your hands-on experience with cloud platforms like AWS or Azure, and how you've used container tools like Docker and Kubernetes to manage applications at scale.
✨Communicate Clearly
Since you'll be guiding both technical teams and non-technical stakeholders, practice explaining complex concepts in simple terms. Think of scenarios where you've successfully communicated technical details to clients or team members who may not have a technical background.
✨Emphasise Collaboration
Highlight your experience working in cross-functional teams. Be prepared to discuss how you've collaborated with engineers, data scientists, and product managers to solve client challenges, and how you’ve mentored junior engineers to foster a positive engineering culture.