Advance finance operations by leading the technical strategy and deployment of cutting-edge AI/ML and agentic solutions. This role sits at the forefront of machine learning innovation, driving product transformation by creating self-sustaining, self-correcting agentic systems. Partner with finance professionals to achieve unparalleled efficiency across the organization.

This position offers a unique opportunity to leverage advanced AI techniques to reshape traditional finance processes into dynamic, AI-native workflows. You will be instrumental in building intelligent systems that not only automate tasks but also collaborate with human experts, unlocking new levels of productivity.

Spearhead the technical design of multi-agent workflows, utilizing a diverse set of tools including ML and Gemini LLMs to tackle complex, multi-layered financial challenges. Develop, prototype, and scale comprehensive AI agents, designing system architectures that prioritize reliability, usability, and auditability. Ensure clear human-in-the-loop interfaces are integrated for finance professionals.

Transition prototypes from isolated testing environments to robust, scaled production systems. Implement high-availability model endpoints with essential features like health checks, error handling, retries, and fallback mechanisms. Establish evaluation frameworks and guardrails to mitigate logical errors, hallucinations, and biases within automated financial decision-making processes. Collaborate closely with Product Managers, Engineers, and Finance stakeholders to translate ambiguous problems into precise technical specifications and overcome system integration hurdles.

A Master's degree in a quantitative field such as Statistics, Engineering, or Sciences, or equivalent practical experience, is required. A minimum of 4 years of experience applying analytics to solve product or business challenges, coupled with proficiency in coding languages like Python, R, and SQL for database querying and statistical analysis, is essential.

Preferred qualifications include 8 years of experience in full-stack development for end-to-end machine learning solutions. Demonstrated experience in building production-ready agentic tools and systems, as well as autonomous or semi-autonomous agents with robust governance, logging, and human-in-loop flows, is highly valued. Expertise in classical ML modeling techniques alongside modern LLM/Generative AI tooling, along with proven success in developing and deploying AI/ML models and utilizing observability/monitoring tools, is also sought. Excellent communication and storytelling abilities, crucial for conveying complex technical concepts to executive leadership, are expected.