AI Engineer Analyst

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. (Both examples are reframed so junior team members contribute meaningfully but are not expected to independently lead full workstreams.)

• 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., 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 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

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.