Join a dynamic team focused on tackling complex business challenges at Google. As an Applied AI/ML Engineer within the Finance Data and AI (DnA) team, you will drive the technical strategy and deployment of cutting-edge AI/ML and agentic solutions. Your role involves transforming traditional finance processes into intelligent, AI-native workflows by building advanced models and self-sustaining agentic systems. You will operate at the forefront of machine learning and product innovation, creating systems that enhance efficiency across Google's finance organization.

This position offers an opportunity to deeply integrate advanced machine learning techniques with product-driven transformation. You will be instrumental in designing and building sophisticated, self-correcting agentic systems that collaborate with finance professionals to achieve unprecedented levels of operational efficiency and insight generation.

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

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

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

Establish rigorous 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 challenges into precise technical specifications. Act as a proactive technical leader, resolving system integration hurdles 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 experience are necessary, focusing on utilizing analytics to solve product or business challenges, with proficiency in coding languages such as Python, R, or SQL, database querying, and statistical analysis.

Preferred qualifications include eight years of experience in full-stack development for comprehensive machine learning solutions. Experience in building production-ready agentic tools and systems, including autonomous or semi-autonomous agents with governance and human-in-the-loop flows, is highly valued. Expertise in both classical ML modeling and modern LLM/Generative AI tooling is essential. Demonstrated success in developing and deploying AI/ML models, alongside experience with observability and monitoring tools to track performance, latency, and model drift, is expected. Excellent communication skills are vital for conveying complex technical concepts to executive leadership.