Applied Artificial Intelligence and Machine Learning Engineer

Role Overview: As an Applied AI and ML Engineer in the Finance Data and Analytics (DnA) team at Google, you will lead the technical strategy, design, and deployment of end-to-end AI/ML and agentic solutions to transform legacy finance processes into AI-native workflows. You will operate at the intersection of advanced machine learning and product-driven transformation. Your main focus will be on building self-sustaining, self-correcting agentic systems that partner with finance Googlers to drive unprecedented efficiency across Google's finance organization. Key Responsibilities: - Lead the technical design of multi-agent workflows, utilizing a various toolkit (ML and Gemini LLMs) to solve complex, multi-layered financial problems. - Build, prototype, and scale end-to-end AI agents. - Outline system architectures that prioritize reliability, usability, and auditability ensuring clear human-in-the-loop interfaces for finance professionals. - Take prototypes from isolated testing environments to scaled production systems. - Design and deploy high-availability model endpoints with health checks, error handling, retries, and fallback mechanisms. - Implement evaluation frameworks and guardrails to eliminate logical errors, hallucinations, and biases in automated financial decision-making. - Partner closely with Product Managers, Engineers, and Finance stakeholders to translate ambiguous finance problems into concrete technical specifications. - Act as a self-sustaining technical leader who helps unblock system integration hurdles in partnership with Engineering teams. Qualifications Required: - Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience. - 3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree. Preferred Qualifications: - Experience working in a financial, audit, or highly regulated domain where deterministic accuracy and auditability are paramount. - Experience in full-stack development for end-to-end machine learning solutions. - Experience with cloud platforms and agentic tools. - Expertise in developing and deploying AI/ML models and utilizing modern observability/monitoring tools to track performance, latency, and model drift. - Strong command of classical ML modeling (e.g., time-series forecasting, tree-based models) alongside modern Large Language Model (LLM)/Generative AI tooling. - Excellent communication and storytelling skills, with a proven ability to translate complex technical architectures and probabilistic model behaviors to executive finance leadership. Role Overview: As an Applied AI and ML Engineer in the Finance Data and Analytics (DnA) team at Google, you will lead the technical strategy, design, and deployment of end-to-end AI/ML and agentic solutions to transform legacy finance processes into AI-native workflows. You will operate at the intersection of advanced machine learning and product-driven transformation. Your main focus will be on building self-sustaining, self-correcting agentic systems that partner with finance Googlers to drive unprecedented efficiency across Google's finance organization. Key Responsibilities: - Lead the technical design of multi-agent workflows, utilizing a various toolkit (ML and Gemini LLMs) to solve complex, multi-layered financial problems. - Build, prototype, and scale end-to-end AI agents. - Outline system architectures that prioritize reliability, usability, and auditability ensuring clear human-in-the-loop interfaces for finance professionals. - Take prototypes from isolated testing environments to scaled production systems. - Design and deploy high-availability model endpoints with health checks, error handling, retries, and fallback mechanisms. - Implement evaluation frameworks and guardrails to eliminate logical errors, hallucinations, and biases in automated financial decision-making. - Partner closely with Product Managers, Engineers, and Finance stakeholders to translate ambiguous finance problems into concrete technical specifications. - Act as a self-sustaining technical leader who helps unblock system integration hurdles in partnership with Engineering teams. Qualifications Required: - Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience. - 3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree. Preferred Qualifications: - Experience working in a financial, audit, or highly regulated domain where deterministic accuracy and auditability are paramount. - Experience in full-stack development for end-to-end machine learning solutions. - Experience with cloud platforms and agentic tools. - Expertise in developing and deploying AI/ML models and utilizing mod