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.
- Other info: Collaborative startup-like environment with exposure to diverse projects.
- Why this job: Make a real impact with AI while learning from industry experts.
- Qualifications: STEM degree or relevant experience in machine learning or software engineering.
The predicted salary is between 30000 - 40000 € per year.
As an early‐career AI Engineer at Cerberus, you will join a small, high‐impact team building AI systems that power decision‐making across a global investment platform. You will 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 will contribute to designing, implementing, and deploying production-grade ML systems—ranging from NLP pipelines to model‐driven workflow automation. You will learn quickly, gain real ownership, and see your work make tangible business impact.
What You Will 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 do not 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 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 London employer: LinkedIn
Cerberus is an exceptional employer for early-career AI Engineers, offering a dynamic work environment where innovation thrives. With a strong focus on collaboration and real-world impact, employees benefit from hands-on experience with cutting-edge AI technologies while working alongside seasoned professionals. The company's commitment to employee growth and development ensures that team members can rapidly advance their skills and careers in a supportive, startup-like atmosphere.
StudySmarter Expert Advice🤫
We think this is how you could land AI Engineer Analyst in London
✨Tip Number 1
Network like a pro! Reach out to current AI engineers or alumni from your university who are in the field. A friendly chat can lead to insider info about job openings and even referrals.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving ML or data engineering. This gives you a chance to demonstrate your hands-on experience and creativity.
✨Tip Number 3
Prepare for interviews by practising common technical questions and coding challenges. Use platforms like LeetCode or HackerRank to sharpen your skills and boost your confidence.
✨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, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace AI Engineer Analyst in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the AI Engineer role. Highlight any relevant projects or internships, especially those involving machine learning or data engineering. We want to see how your background aligns with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to show us your personality and passion for AI. Share why you're excited about this role and how you can contribute to our team. Keep it concise but impactful—let us know why you're the perfect fit!
Showcase Your Projects:If you've worked on any personal or academic projects related to AI, make sure to mention them! We love seeing practical applications of your skills, so include links or descriptions of your work. This helps us understand your hands-on experience.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're serious about joining our team at StudySmarter!
How to prepare for a job interview at LinkedIn
✨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 any relevant projects you've worked on. This will show that you have a solid foundation and are eager to apply your knowledge.
✨Showcase Your Projects
Prepare to talk about specific projects or internships where you've applied your skills. Highlight your role, the challenges you faced, and how you contributed to the success of the project. This gives interviewers insight into your practical experience and problem-solving abilities.
✨Ask Insightful Questions
Come prepared with questions that demonstrate your curiosity about the company and the role. Inquire about the team’s current projects, the tools they use, or how they measure success in their AI initiatives. This shows you're genuinely interested and engaged.
✨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. Clear communication is key in collaborative environments, so make sure you can articulate your thoughts effectively.
