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For Companies At a Glance
- Tasks: Build and deliver cutting-edge ML systems that drive investment decisions.
- Company: Join a dynamic team at a leading global investment firm.
- Benefits: Competitive salary, hands-on experience, 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 30000 - 40000 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;
- 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 (especially Python): Experience writing clean, maintainable Python code.
- Applied AI experience: 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: 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.
Locations
AI Engineer Analyst in City of London, 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, 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 gives you a chance to demonstrate your hands-on experience and problem-solving abilities.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills, especially in Python. Practice common algorithms and data structures, and be ready to discuss your past projects and how they relate to the job.
✨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 Cerberus.
We think you need these skills to ace AI Engineer Analyst in City of London, 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, let us know! We love seeing practical applications of your skills.
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’s super easy!
How to prepare for a job interview at Cerberus Capital Management
✨Know Your AI Fundamentals
Brush up on your machine learning concepts and algorithms. Be ready to discuss your understanding of ML frameworks, data pipelines, and how you’ve applied these in past projects or internships. This will show that you have a solid foundation and are eager to build on it.
✨Showcase Your Projects
Prepare to talk about any relevant projects you've worked on, especially those involving Python and AI. Highlight your role, the challenges you faced, and how you overcame them. This gives interviewers insight into your practical experience and problem-solving skills.
✨Ask Insightful Questions
Come prepared with questions that demonstrate your curiosity about the team and the work they do. Inquire about their current projects, the tools they use, or how they approach integrating AI into business workflows. This shows you're genuinely interested in the role and eager to learn.
✨Communicate Clearly
Practice explaining complex technical concepts in simple terms. You’ll need to translate your findings for both technical and non-technical team members. Being able to communicate effectively is key in a collaborative environment like this one.