Talensa Partners
AI Solutions Engineer - AI Deployed - Investment Firm
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AI Solutions Engineer - AI Deployed - Investment Firm
AI Engineer – Investment Manager
Machine Learning & Applied AI (Embedded GenAI and Agentic AI) London or New York | Competitive Base Salary + Bonus | Hybrid Working
About the Role
An opportunity to join an agile, high-impact AI & Tech team that collaborates directly with investment operations, trading technology, portfolio management, business analytics, and cross-functional stakeholders. The team accelerates decision-making, automates workflows, and drives measurable business value by building and deploying AI-driven and machine learning systems.
The team is dynamic but established—fast-moving enough to innovate, yet grounded enough to ensure process buy-in and lasting influence. You’ll work intimately with data scientists, software engineers, and technology/investment partners across a broad spectrum, including investment operations, deal teams, portfolio management, and internal systems.
What You’ll Build
- Production-grade ML & AI systems that influence investment decisions
- NLP pipelines to extract structured insights from unstructured financial documents
- Generative AI applications to automate due diligence, deal sourcing, and investment research
- Automated data pipelines integrating signals from external sources via APIs, enriched, and surfaced for internal platforms
- ML models for forecasting, classification, and optimisation deployed in live investment workflows, ensuring measurable adoption
- Agent-based and LLM-powered systems integrated with existing investment infrastructure to streamline operations
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Required Technical Stack & Experience
- Core Python skills: NumPy, pandas, scikit-learn
- Deep Learning expertise: PyTorch or equivalent
- LLM integration: Proficiency with OpenAI, Anthropic, or similar APIs
- Backend development: FastAPI
- ML Deployment: MLOps practices, including production model deployment
- SQL mastery for data pipeline management
- Cloud Infrastructure: Azure (mandatory), with preference for AWS/GCP
- Containerisation & orchestration: Docker and Kubernetes
- Version control & CI/CD: Git and Azure DevOps
Requirements
- A degree in Computer Science, Financial Engineering, or equivalent hands-on experience with applied statistics, machine learning, NLP, forecasting, or optimisation.
- Production-grade Python knowledge: Understanding of language nuances, clear type hints, and maintainable code structures.
- ML deployment experience: Training is not enough—you must integrate models into APIs/microservices and monitor performance in production.
- SQL & pipeline management: Ability to design and maintain data workflows for analytics and modelling.
- MLOps familiarity: Experience with experiment tracking, model versioning, and production monitoring.
- System integration: Working closely with engineering teams to ensure scalable, maintainable deployments.
- LLM/AI agent experience: Prior work with LLMs, artificial agents, or workflow orchestration.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Strong advantage if you have:
- Investment/networking experience (private equity, hedge funds, asset management).
- Financial expertise (deal flow, portfolio metrics, markets data, credit, or operational datasets).
- Statistical programming (NumPyro, PyMC).
- Infrastructure-as-Code (Terraform).
Why Join?
- Competitive salary + performance bonus + potential equity participation.
- Hybrid work flexibility between New York or London.
- Small team, broad mandate, and scope to drive value in a greenfield AI environment.
If your background aligns with building, deploying, and scaling AI systems in fast-moving investment environments—and you’re ready to apply that experience to a forward-thinking firm—share your CV.
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