Machine Learning & Applied AI (Embedded GenAI and Agentic AI)
London or New York | Competitive Base Salary + Bonus
3 to 6 Years AI / ML Engineering Experience | Investment firm
Hybrid Working
Opportunity to join an agile, high-impact AI & Tech team mandated to work directly with investment operations, trading technology, portfolio management, business analytics and cross-functional teams to build and deploy AI driven & machine learning systems that accelerate / aid decision-making, automate complex workflows and unlock measurable business value.
The team:
New enough to move fast but established to have process buy-in and enduring influence. You will work with data scientists, software engineers, technology and investment partners across a broad remit that spans investment operations, deal teams, portfolio management and internal systems. The culture is collaborative, dedicated and ambitious to deliver incremental gains.
What you will be building:
- Production-grade ML and AI systems that directly impact investment decisions
- NLP pipelines that extract structured insight from complex, unstructured financial documents.
- Generative AI applications that automate and accelerate due diligence, deal sourcing and investment research workflows.
- Automated data pipelines that integrate signals from external sources, enrich them via third-party APIs and surface them through internal platforms.
- ML models across forecasting, classification and optimisation deployed into live investment workflows with measurable adoption and business impact.
- Agent-based and LLM-powered systems that integrate with existing investment infrastructure to streamline operational processes.
Stack experience you’ll need:
- Python expertise at the core including NumPy, pandas, scikit-learn
- Deep learning via PyTorch or equivalent
- LLM APIs including OpenAI, Anthropic or equivalent.
- FastAPI for backend and service development.
- Familiar deploying ML models into production – MLOps
- Cloud infrastructure on Azure, with AWS or GCP
- Docker and Kubernetes for containerisation and deployment.
- Git and Azure DevOps for version control and CI/CD.
What they are looking for:
- A degree in Computer Science or Financial Engineering – equivalent hands‑on experience with applied statistics, machine learning, NLP, forecasting or optimisation.
- Strong, production‑quality Python. You understand the language’s limitations, write with type hints, and build things that other engineers can maintain and extend.
- Experience deploying ML models into production via APIs or microservices, not just training them in a research environment.
- Proficiency in SQL and the ability to build and manage data pipelines for analytics and modelling workflows.
- Familiarity with MLOps practices including experiment tracking, model versioning and performance monitoring in production.
- Comfort integrating ML components into broader systems and working closely with engineering teams on scalable, maintainable deployment.
- Experience working with LLM APIs and building AI agents or orchestration workflows.
- A pragmatic mindset. You care about impact, not elegance for its own sake. You want to see your work deployed, adopted and driving real results.
Strong advantage:
- Prior experience inside an investment management, private equity, hedge fund or asset management environment.
- Familiarity with financial data — deal flow, portfolio metrics, market data, credit or operational datasets.
- Experience with statistical programming libraries such as NumPyro or PyMC.
- Familiarity with infrastructure as code tools such as Terraform.
The opportunity:
Competitive base salary plus performance bonus and future equity participation. Hybrid working across New York or London. A small team, a broad mandate and clear opportunity to create value.
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