Google

Runs Search and YouTube and builds Gemini AI

Applied AI and ML Engineer

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

A Master's degree in a quantitative discipline such as Statistics, Engineering, or Sciences, or equivalent practical experience.
At least 3 years of experience using analytics to solve product or business problems, coding in Python, R, or SQL, querying databases, or performing statistical analysis; alternatively, a PhD degree in a quantitative discipline such as Statistics, Engineering, or Sciences.
At least 1 year of experience building agentic systems.

Responsibilities

Contribute to the technical design of multi-agent workflows using machine learning and Gemini large language models to solve complex, multi-layered financial problems.
Build, prototype, and scale end-to-end AI agents, collaborating with senior developers on robust system architectures that prioritize reliability, usability, and auditability and provide clear human-in-the-loop interfaces for finance professionals.
Move prototypes from isolated testing environments into scaled production systems and collaborate with senior developers to design and deploy high-availability model endpoints with health checks, error handling, retries, and fallback mechanisms.
Implement evaluation frameworks and guardrails to prevent logical errors, hallucinations, and biases in automated financial decision-making.
Lead the technical strategy, design, and deployment of end-to-end AI, machine learning, and agentic solutions that transform legacy Finance processes into AI-native workflows.
Design, develop, and deploy large-scale autonomous, self-correcting agentic systems for Finance processes including Accounting, Finance Operations, and financial planning and analysis.

Desired Qualifications

At least 4 years of experience using analytics to solve product problems, coding in Python, R, or SQL, querying databases, or performing statistical analysis, or a relevant PhD degree.
Experience in a financial, audit, or highly regulated domain where deterministic accuracy and auditability are paramount.
Expertise in developing and deploying AI or machine learning models and using observability and monitoring tools to track performance, latency, and model drift.
Strong command of classical machine learning modeling, including time-series forecasting and tree-based models, alongside modern large language model and generative AI tooling.

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.

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.

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.

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.

About the Company

Google

Runs Search and YouTube and builds Gemini AI

Google runs the world's most used search engine and free services such as YouTube, Android, and Gmail, and it builds the Gemini AI models that work inside them. In 2017, its researchers invented the transformer, the neural network design behind ChatGPT and most other AI chatbots. People use these services without paying, and Google earns most of its money from ads placed beside search results and YouTube videos. Businesses rent computing power and Gemini models through Google Cloud, and Google also sells subscriptions and Pixel phones. Larry Page and Sergey Brin founded Google in 1998 as Stanford PhD students; it is based in Mountain View, California, United States, and is the main business of parent company Alphabet.

Company size: 10,001+
Company stage: IPO
Headquarters: Mountain View, CA
Founded: 1998

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