Join a dynamic team at the forefront of transforming legacy finance processes into AI-native workflows. This role involves leading the technical strategy, design, and deployment of cutting-edge AI/ML and agentic solutions. You will operate at the exciting intersection of advanced machine learning and product-driven transformation, building intelligent systems that partner with finance professionals to drive unprecedented efficiency.

This position is ideal for experienced engineers passionate about leveraging AI to solve complex business challenges. You will have the opportunity to shape the future of finance operations through innovative technology.

In most instances, in-person interviews are a part of the hiring process.

Spearhead the technical design of multi-agent workflows, employing diverse toolkits, including ML and Gemini LLMs, to tackle intricate, multi-layered financial problems.

Develop, prototype, and scale end-to-end AI agents, crafting system architectures that prioritize reliability, usability, and auditability. Ensure clear human-in-the-loop interfaces for finance professionals.

Transition prototypes from isolated testing environments to robust, scaled production systems. Deploy high-availability model endpoints complete with health checks, error handling, retries, and fallback mechanisms.

Implement comprehensive evaluation frameworks and guardrails to mitigate logical errors, hallucinations, and biases in automated financial decision-making.

Collaborate closely with Product Managers, Engineers, and Finance stakeholders to translate ambiguous finance problems into precise technical specifications. Serve as a self-sustaining technical leader, actively resolving system integration hurdles in partnership with Engineering teams.

A Master's degree in a quantitative discipline such as Statistics, Engineering, or Sciences, or equivalent practical experience is required.

Four years of experience are necessary, focusing on applying analytics to solve product or business challenges, coupled with proficiency in coding languages like Python, R, and SQL, and experience querying databases or performing statistical analysis.

Preferred qualifications include eight years of experience in full-stack development for end-to-end machine learning solutions. Experience building production-ready Agentic tools and systems, including autonomous or semi-autonomous agents with governance and human-in-loop flows, is highly valued.

Expertise in classical ML modeling, such as time-series forecasting and tree-based models, alongside modern Large Language Model (LLM)/Generative AI tooling, is a significant asset. Demonstrated success in developing and deploying AI or ML models, utilizing modern observability/monitoring tools to track performance, latency, and model drift is also preferred.

Excellent communication and storytelling abilities are essential, enabling the translation of complex technical architectures and probabilistic model behaviors to executive finance leadership.