Join our Finance Data and AI (DnA) team as an Applied AI/ML Engineer and spearhead the technical strategy for AI/ML and agentic solutions. Transform traditional finance processes into AI-native workflows by operating at the forefront of advanced machine learning and product-driven innovation. You will be instrumental in building sophisticated models and designing self-sustaining, self-correcting agentic systems. These systems will empower finance professionals at Google, driving exceptional efficiency across the organization.

This role offers a unique opportunity to immerse yourself in complex business challenges, drawing insights from data analysis to develop impactful recommendations. You will then articulate these strategies to senior executives, facilitate their implementation, and track the resulting impact.

Lead the technical design of intricate multi-agent workflows, leveraging a diverse toolkit including ML and Gemini LLMs to tackle multi-layered financial challenges. Develop, prototype, and scale end-to-end AI agents, meticulously outlining system architectures for optimal 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. Design and deploy high-availability model endpoints featuring comprehensive health checks, error handling, retries, and fallback mechanisms. Implement sophisticated 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 financial problems into precise technical specifications. Function as a self-sustaining technical leader, adept at resolving system integration hurdles in partnership with Engineering teams.

A Master's degree in a quantitative field such as Statistics, Engineering, or Sciences, or equivalent practical experience, is required. You should possess at least 4 years of experience applying analytics to solve product or business issues. Proficiency in coding languages like Python, R, and SQL, along with experience querying databases or conducting statistical analysis, is essential.

Preferred candidates will have 8 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, logging, and human-in-the-loop capabilities, is highly valued. Expertise in classical ML modeling alongside modern LLM/Generative AI tooling is a significant advantage.

Demonstrated success in developing and deploying AI or ML models, coupled with experience utilizing modern observability and monitoring tools to track performance, latency, and model drift, is expected. Excellent communication and storytelling abilities, enabling the translation of complex technical concepts to executive leadership, are crucial for this role.