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- Tasks: Build and deliver cutting-edge ML systems for global investment decision-making.
- Company: Join a dynamic team at Cerberus, transforming AI in finance.
- Benefits: Gain hands-on experience, competitive salary, and rapid career growth.
- Why this job: Make a real impact with AI while learning from industry experts.
- Qualifications: STEM degree or relevant experience in ML, software, or data engineering.
- Other info: Collaborative startup-like environment with exposure to diverse projects.
The predicted salary is between 28800 - 48000 ÂŁ per year.
As an early‑career AI Engineer at Cerberus, you’ll join a small, high‑impact team building AI systems that power decision‑making across a global investment platform. You’ll work alongside experienced AI engineers, data scientists, and technologists to deliver real products used by investment teams and portfolio companies.
This role is ideal for:
- PhD graduates in a STEM field with applied ML, optimization, or computational experience; or
- Bachelor’s/Master’s graduates with 1–2 years of industry experience or relevant internships in machine learning, data engineering, or software engineering.
You’ll contribute to designing, implementing, and deploying production-grade ML systems—ranging from NLP pipelines to model‑driven workflow automation. You’ll learn quickly, gain real ownership, and see your work make tangible business impact.
What You’ll Do
- Build and deliver ML systems: Work with senior engineers to design, train, and deploy machine learning models and data-driven tools that support investment and operational decision-making.
- Contribute to real production deployments: Help integrate ML models into business workflows, build data pipelines, and support the rollout of AI applications across teams.
- Experiment and iterate: Prototype ideas, test assumptions, and rapidly evolve solutions based on real user feedback and real-world constraints.
- Learn modern tooling and practices: Gain hands-on experience with ML frameworks, cloud infrastructure, MLOps tools, and best practices for building scalable AI systems.
- Communicate clearly: Translate technical findings into clear, structured insights for collaborators across technical and business teams.
- Grow as an AI engineer: Develop skills across the full ML lifecycle—data processing, modelling, evaluation, deployment, and ongoing improvement.
Sample Projects You Might Work On
- GenAI for due diligence: Support the configuration, extension, and rollout of our in-house GenAI platform across investment teams. Work with senior engineers to customise workflows, analyse model outputs, and drive adoption.
- Automated Deal Sourcing Tools: Help build prototypes that extract signals from datasets and integrate with APIs to enrich leads. Support the creation of modular ML-driven components that can be used across investment strategies.
Your Experience
We don’t expect candidates to have experience across all areas—what matters most is strong technical fundamentals, curiosity, and a willingness to learn quickly.
Foundational skills
- Degree in a STEM field.
- PhD candidates: applied research involving ML, optimisation, simulation, statistics, numerical methods, NLP, or related areas.
- Bachelor’s/Master’s: 1–2 years of industry experience or relevant internships in ML, software engineering, or data engineering.
Programming experience
- Experience writing clean, maintainable Python code.
- Applied AI experience such as exposure to LLM APIs (OpenAI, Azure OpenAI, Anthropic, etc.) and experience with small personal or internship projects building agents or AI-driven workflows.
- Agentic frameworks in Python is a plus but not required.
Data and analytical skills
- Comfortable working with data, performing analysis, and writing SQL queries.
- Experience building simple data pipelines or transformation workflows is a plus.
- Exposure to ML Ops or production systems (nice to have): Familiarity with tools like MLflow, Weights & Biases, or cloud platforms (Azure, AWS, or GCP).
- Experience deploying models via APIs or lightweight services is a bonus, not a requirement.
Software engineering basics
- Understanding of Git/GitHub/Azure DevOps, testing basics, and general good engineering practices.
Mindset
- Strong problem-solving skills.
- Curiosity and eagerness to learn.
- Pragmatic, impact-driven approach.
- Ability to work collaboratively in a fast-paced environment.
About Us
We are a growing team of AI specialists—data scientists, ML engineers, software engineers, and technology strategists—working to transform how a global investment firm with $65B+ in assets uses data and AI. We operate like a startup within the firm: fast, collaborative, and focused on delivering real value. Our work spans investment desks, portfolio companies, and core operations, giving early-career engineers wide exposure and the opportunity to grow rapidly.
AI Engineer Analyst in City of London employer: Cerberus Capital Management
Contact Detail:
Cerberus Capital Management Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Engineer Analyst in City of London
✨Tip Number 1
Network like a pro! Reach out to current AI engineers or analysts on LinkedIn, and don’t be shy about asking for informational interviews. It’s a great way to learn more about the role and get your name out there.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving machine learning or data engineering. This will give you a leg up when discussing your experience during interviews.
✨Tip Number 3
Practice makes perfect! Prepare for technical interviews by solving coding challenges and brushing up on your Python skills. Websites like LeetCode or HackerRank can be super helpful for this.
✨Tip Number 4
Apply through our website! We love seeing candidates who are genuinely interested in joining our team. Tailor your application to highlight how your skills align with the role of an AI Engineer Analyst.
We think you need these skills to ace AI Engineer Analyst in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the AI Engineer role. Highlight any relevant projects or internships, especially those involving machine learning or data engineering.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about AI and how your background makes you a great fit for our team. Be sure to mention specific projects or experiences that showcase your skills.
Showcase Your Technical Skills: Don’t shy away from detailing your programming experience, especially in Python. If you've worked with ML frameworks or cloud platforms, make sure to include that too—it's what we love to see!
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 Cerberus Capital Management
✨Know Your AI Fundamentals
Brush up on your machine learning concepts, especially those related to NLP and model deployment. Be ready to discuss your academic projects or internships where you applied these skills, as this will show your understanding and passion for the field.
✨Showcase Your Coding Skills
Since programming in Python is crucial for this role, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice writing clean, maintainable code and be familiar with Git for version control.
✨Prepare for Real-World Scenarios
Think about how you would approach integrating ML models into business workflows. Be ready to discuss any relevant projects where you built data pipelines or worked with APIs, as this will highlight your practical experience and problem-solving skills.
✨Communicate Clearly and Confidently
You’ll need to translate complex technical findings into insights for non-technical team members. Practice explaining your past projects in simple terms, focusing on the impact of your work and how it contributed to decision-making processes.