Lead the technical design of multi-agent workflows, utilizing a various toolkit (ML and Gemini LLMs) to solve complex, multi-layered financial problems.
Build, prototype, and scale end-to-end AI agents. Outline system architectures that prioritize reliability, usability, and auditability ensuring clear human-in-the-loop interfaces for finance professionals.
Take prototypes from isolated testing environments to scaled production systems. Design and deploy high-availability model endpoints with health checks, error handling, retries, and fallback mechanisms.
Implement evaluation frameworks and guardrails to eliminate logical errors, hallucinations, and biases in automated financial decision-making.
Partner closely with Product Managers, Engineers, and Finance stakeholders to translate ambiguous finance problems into concrete technical specification. Act as a self-sustaining technical leader who helps unblock system integration hurdles in partnership with Engineering teams.
Minimum qualifications:
Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
Preferred qualifications:
Experience working in a financial, audit, or highly regulated domain where deterministic accuracy and auditability are paramount.
Experience in full-stack development for end-to-end machine learning solutions.
Demonstrated expertise in developing and deploying AI or ML models and utilizing modern observability/monitoring tools to track performance, latency, and model drift.
Expertise in classical ML modeling (e.g., time-series forecasting, tree-based models) alongside modern Large Language Model (LLM)/Generative AI tooling.
Excellent communication and storytelling skills, with a proven ability to translate complex technical architectures and probabilistic model behaviors to executive finance leadership.
