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Open nowFirst seen 9 hours ago

Applied AI and ML Engineer

Google3,393 open roles

Where
Bengaluru, Karnataka, India
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Your applicationOpen nowApplied AI and ML EngineerGoogle · Bengaluru, Karnataka, India

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Google's own form.

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The clock on this job

Early applications get read.

7.7% of postings close within 7 days. Measured by our own scanner across the market. Google postings stay open a median of 26 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.3%3 days
  3. 7.7%7 days
  4. 14.0%14 days
  5. 33.7%30 days
This job: first seen 9 hours ago

Google median: 26 days open

The posting

In most instances, this position requires in-person interviews as part of the hiring process.

Minimum qualifications:

  • 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 PhD degree in a quantitative discipline (e.g., Statistics, Engineering, Sciences).
  • 1 year of experience building agentic systems.

Preferred qualifications:

  • 4 years of experience using analytics to solve product problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.
  • Experience working in a financial, audit, or highly regulated domain where deterministic accuracy and auditability are paramount.
  • Demonstrated expertise in developing and deploying AI or ML models and utilizing modern observability/monitoring tools to track performance, latency, model drift.
  • Excellent communication skills, with a proven ability to translate complex technical architectures and probabilistic model behaviors.
  • Strong command of classical ML modeling (e.g., time-series forecasting, tree-based models) alongside modern LLM/Generative AI tooling.

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.

At Google, data drives all of our decision-making. As part of the Finance Data and AI (DnA) team in the CFO organization, our focus is on building secure, scalable, and intelligent solutions that transform financial operations.

In this role, 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 not just build models; you will design autonomous, self-correcting agentic systems that partner with Finance Googlers to drive unprecedented efficiency across Google's Finance organization. You will operate at the intersection of advanced ML and Agentic tools to design, develop and deploy large-scale solutions for various Finance processes like Accounting, Finance Operations, financial planning and analysis, etc.

Responsibilities

  • Contribute to the technical design of multi-agent workflows, utilizing a toolkit (ML and Gemini LLMs) to solve complex, multi-layered financial problems.
  • Build, prototype, and scale end-to-end AI agents. Collaborate with senior developers to build robust 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. Collaborate with senior developers to design and deploy high-availability model endpoints with robust health checks, error handling, retries, and fallback mechanisms.
  • Implement rigorous evaluation frameworks and guardrails to eliminate logical errors, hallucinations, and biases in automated financial decision-making.
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