Senior Applied AI/ML Engineer
Senior engineer on Google's Finance Data and AI team who designs, builds, and scales end-to-end AI/ML and multi-agent (LLM/Gemini) systems to automate and transform finance workflows into AI-native processes. Day-to-day spans prototyping agents, hardening production endpoints, evaluation/monitoring, and partnering with finance stakeholders.
- Compensation
- Not disclosed
- Location
- Bengaluru, Karnataka, India
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 4+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 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
Nice to have
- 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 LLM/Generative AI tooling
- Expertise in developing and deploying AI/ML models using modern observability/monitoring tools to track performance, latency, and model drift
- Excellent communication and storytelling skills to translate technical architectures and probabilistic model behaviors to executive finance leadership
Full description
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.
As an Applied AI/ML Engineer, in Finance Data and AI (DnA) team, 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. You will build models, design self-sustaining, self-correcting agentic systems that partner with finance Googlers to drive unprecedented efficiency across Google's finance organization.
- 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.