Partner with business team to identify and prioritize high-impact AI use cases, including buy-vs-build recommendations.
Design and deliver AI products end-to-end, from problem definition through prototype, pilot, and production handover.
Build AI-powered solutions.
Develop robust datasets along with retrieval and reasoning pipelines across different business functionalities.
Integrate with vendor AI platforms and internal AI solutions, reusing existing capabilities where possible.
Instrument solutions to track adoption, quality, business value, ROI, cost, and risk.
Partner with technology team to ensure secure, compliant, auditable, and supportable solutions.
Run rapid evaluation cycles and contribute reusable components, patterns, and tools for wider adoption.
Requirements
Bachelor's or Master's degree in Data Science, Computer Science, Artificial Intelligence / Machine Learning, Software Engineering, Quantitative Finance, or a related discipline
Background in the financial industry with a solid understanding of financial markets, and 5+ years of professional experience in traditional software engineering or a similar role.
Demonstrated experience (minimum 2 years) building and shipping ML/AI products in production environments, ideally with measurable business outcomes.
Strong proficiency in a modern development language such as Python.
Hands-on experience with modern databases and data platforms such as PostgreSQL, MS SQL, Snowflake, or comparable technologies.
Strong hands-on engineering ability to deliver LLM applications end-to-end (e.g., Python, APIs, system integration, testing, deployment readiness).
Hands-on experience designing and developing software solutions in enterprise or team-based environments, with a strong focus on code quality, sound design decisions, and collaboration.
Familiarity with the software development lifecycle, version control systems, and modern DevOps practices.
Ability to translate ambiguous business problems into product scopes, roadmaps, and iteration plans with clear success metrics and value gates.
Comfortable working with sensitive/confidential data and partnering with governance, risk, and security stakeholders to embed controls from the start.
Strong collaboration and communication skills—able to work embedded within business teams and cross-functionally with other technology teams.