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- Tasks: Build and deploy impactful AI solutions for diverse clients in the public sector.
- 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-world impact with AI while collaborating with talented teams.
- Qualifications: Experience in machine learning, Python, and cloud platforms required.
- Other info: Diverse and inclusive workplace with excellent career growth opportunities.
The predicted salary is between 60000 - 80000 £ 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 Public Services Business Unit is committed to leveraging AI for the benefit of individual citizens and the public good. From our work informing strategic government decisions, to optimising our NHS, through to reducing bureaucratic backlogs - we know that AI offers opportunities to drive improvements at every level of Government and we are proud to lead on some of the most impactful work happening in the sector. Because of the nature of the work we do with our Government clients, you may need to be eligible for UK Security Clearance (SC) and willing to work on site with these clients from time to time.
About the role: Join us as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for our diverse clients. You will be instrumental in bringing machine learning out of the lab and into the real world, contributing to scalable software architecture and defining best practices. Working with clients, and cross-functional teams, you'll ensure technical feasibility and timely delivery of high-quality, production-grade ML systems.
What you’ll be doing:
- Building and deploying production-grade ML software, tools, and infrastructure.
- Creating reusable, scalable solutions that accelerate the delivery of ML systems.
- Collaborating with engineers, data scientists, and commercial leads to solve critical client challenges.
- Leading technical scoping and architectural decisions to ensure project feasibility and impact.
- Defining and implementing Faculty’s standards for deploying machine learning at scale.
- Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders.
Who we’re looking for: You understand the full machine learning lifecycle and have experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch. You possess strong Python skills and solid experience in software engineering best practices. You bring hands-on experience with cloud platforms and infrastructure (e.g., AWS, Azure, GCP), including architecture and security. You've worked with container and orchestration tools such as Docker & Kubernetes to build and manage applications at scale. You are comfortable with core ML concepts, including probability, statistics, and common learning techniques. You're an excellent communicator, able to guide technical teams and confidently advise non-technical stakeholders. You thrive in a fast-paced environment, and enjoy the autonomy to own scope, solve and deliver solutions.
Our 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
If you don’t feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please don’t hesitate in applying as you might be right for this role, or other roles. We are open to conversations about part-time hours.
Production ML Engineer: Real-World AI for Public Sector in London employer: The Rundown AI, Inc.
Contact Detail:
The Rundown AI, Inc. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Production ML Engineer: Real-World AI for Public Sector 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. A personal introduction can make all the difference when applying for that Production ML Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those using frameworks like TensorFlow or PyTorch. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for technical interviews by practicing pair programming and system design scenarios. Brush up on your Python skills and be ready to discuss your approach to building scalable ML systems. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows you’re genuinely interested in joining Faculty and contributing to impactful AI solutions.
We think you need these skills to ace Production ML Engineer: Real-World AI for Public Sector in London
Some tips for your application 🫡
Show Your Passion for AI: When writing your application, let us see your enthusiasm for AI and its real-world applications. Share any projects or experiences that highlight your commitment to using technology for the public good.
Tailor Your CV and Cover Letter: Make sure to customise your CV and cover letter to reflect the specific skills and experiences mentioned in the job description. We want to see how your background aligns with our mission and the role of a Production ML Engineer.
Highlight Technical Skills: Don’t forget to showcase your technical expertise! Mention your experience with machine learning frameworks, cloud platforms, and software engineering best practices. This is your chance to shine!
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. Don’t miss out!
How to prepare for a job interview at The Rundown AI, Inc.
✨Know Your ML Frameworks
Make sure you’re well-versed in the machine learning frameworks mentioned in the job description, like Scikit-learn, TensorFlow, and PyTorch. Brush up on your practical experience with these tools, as you might be asked to demonstrate your knowledge during the technical interviews.
✨Showcase Your Python Skills
Since strong Python skills are crucial for this role, prepare to discuss your past projects and how you've applied Python in real-world scenarios. Be ready to solve coding challenges that test your understanding of software engineering best practices.
✨Understand Cloud Platforms
Familiarise yourself with cloud platforms like AWS, Azure, or GCP, especially their architecture and security aspects. You may be asked about your experience deploying ML solutions in the cloud, so have specific examples ready to share.
✨Communicate Effectively
As an excellent communicator, you’ll need to explain complex ML concepts to non-technical stakeholders. Practice articulating your thoughts clearly and concisely, and think of ways to simplify technical jargon into relatable terms.