Join a dynamic team at Google focused on tackling complex business challenges through innovative AI and Machine Learning solutions. As an Applied AI/ML Engineer on the Finance Data and AI (DnA) team, you will spearhead the technical vision, design, and deployment of cutting-edge AI/ML and agentic systems. Your work will revolutionize legacy finance processes, transforming them into intelligent, AI-native workflows.

This role places you at the forefront of advanced machine learning and product transformation. You will develop sophisticated models and engineer self-sustaining, self-correcting agentic systems designed to collaborate with finance professionals, driving unparalleled efficiency across Google's finance operations.

Lead the technical design and implementation of multi-agent workflows, leveraging a diverse toolkit including ML and Gemini LLMs to address intricate, multi-layered financial challenges.

Develop, prototype, and scale end-to-end AI agents. Architect systems that prioritize robustness, user-friendliness, and auditability, ensuring intuitive human-in-the-loop interfaces for finance teams.

Transition prototypes from isolated testing environments to fully scaled production systems. Implement high-availability model endpoints featuring health checks, error management, retry mechanisms, and fallback strategies.

Establish robust evaluation frameworks and guardrails to mitigate logical errors, hallucinations, and biases in automated financial decision-making processes.

Collaborate closely with Product Managers, Engineers, and Finance stakeholders to translate ambiguous financial problems into precise technical specifications. Serve as a proactive technical leader, resolving system integration challenges in partnership with Engineering teams.

A Master's degree in a quantitative field like Statistics, Engineering, or Sciences, or equivalent practical experience, is required.

Four years of hands-on experience are necessary, encompassing the application of analytics to solve product or business problems, proficiency in coding languages such as Python, R, and SQL, database querying, and statistical analysis.

Eight years of experience in full-stack development for comprehensive machine learning solutions is preferred.

Demonstrated experience in building production-ready agentic tools and systems, not just proof-of-concepts.

Experience developing autonomous or semi-autonomous agents with integrated governance, logging, and human-in-the-loop workflows.

Expertise in both classical ML modeling (e.g., time-series forecasting, tree-based models) and modern Large Language Model (LLM)/Generative AI tooling.

Proven ability to develop and deploy AI or ML models, coupled with the utilization of contemporary observability and monitoring tools to track performance, latency, and model drift.

Exceptional communication and storytelling skills, with the capacity to convey complex technical architectures and probabilistic model behaviors to executive finance leadership.