Join a leading technology firm as an Applied AI and ML Engineer, where you'll tackle complex business challenges by developing innovative AI and ML solutions. This role is within the Finance Data and AI (DnA) team, focused on transforming financial operations through secure, scalable, and intelligent systems. You will architect and deploy autonomous agentic systems, integrating advanced machine learning with cutting-edge tools to enhance efficiency across Google's Finance organization.

Lead the technical strategy for designing and deploying end-to-end AI/ML and Agentic solutions, transforming legacy financial processes into AI-native workflows. Contribute to the technical design of multi-agent workflows using ML and Gemini LLMs to address intricate financial problems. Build, prototype, and scale AI agents, collaborating with senior developers on robust system architectures emphasizing reliability and auditability.

Transition prototypes from isolated environments to scaled production systems. Design and implement high-availability model endpoints with comprehensive health checks and error handling. Develop rigorous evaluation frameworks and guardrails to ensure accuracy and mitigate bias in automated financial decision-making.

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

Candidates need 3 years of experience in leveraging analytics for business problems, including proficiency in coding (Python, R, SQL), database querying, or statistical analysis. Additionally, 1 year of experience building agentic systems is essential.

Preferred qualifications include 4 years of analytics experience or a relevant PhD, alongside experience in financial, audit, or regulated sectors where accuracy and auditability are critical. Expertise in deploying AI/ML models, utilizing modern monitoring tools, and strong communication skills for translating complex technical concepts are highly valued.