Applied AI/ML Engineer

About the job:
Google's leadership team hand-picks thorny business challenges, and members of BizOps work in small teams to find solutions. As part of this team you fully immerse yourself in data collection, draw insight from analysis, and then zoom out to develop compelling, synthesized recommendations. Taking strategy one step further, you also persuasively communicate your recommendations to senior-level executives, roll-up your sleeves to help drive implementation and check back-in to see the impact of your recommendations.

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 specification. Act as a self-sustaining technical leader who helps unblock system integration hurdles in partnership with Engineering teams.

Minimum qualifications:
Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.

Preferred qualifications:
8 years of experience in full-stack development for end-to-end machine learning solutions.
Experience building Agentic tools and systems (production-ready, not POCs).
Experience building autonomous or semi-autonomous agents with governance, logging, and human-in-loop flows.
Experience in classical ML modeling (e.g., time-series forecasting, tree-based models) alongside modern Large Language Model (LLM)/Generative AI tooling.
Demonstrated expertise in developing and deploying AI or ML models and utilizing modern observability/monitoring tools to track performance, latency, and model drift.
Excellent communication and storytelling skills, with an ability to translate complex technical architectures and probabilistic model behaviors to executive finance leadership.