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Google Product Manager Jobs in Indiana (NOW HIRING)

Deloitte

Google AI Lead Architect

Indianapolis, IN · On-site

$52.75 - $72.50/hr

We transform engineering teams, modernize technology, and deliver complex programs with a product ... implement context management, retrieval strategies, and observability. * Define end-to-end ...

Viktor

Performance Marketing Lead (Google Ads)

Warsaw, IN · On-site

$120 - $180/hr

You'll also manage the Google Specialist: you set the playbook and the bar, they run the account ... The product makes the work easier. The right buyer converts when they land. Your job is making sure ...

Google

Data Center Electrical Engineer

Fort Wayne, IN · On-site

... management or Supervisory Control and Data Acquisition (SCADA) tools. * Experience working with ... more about benefits at Google . Responsibilities * Define data center system-level, product ...

Festool USA

Digital Platform Manager

Lebanon, IN · On-site

Own and manage the digital product roadmap for Festool North America's websites and connected ... Google Analytics, Shopify, or similar tools. * Proven ability to translate business needs into ...

Hilltop Holdings

Production Manager, NE

Richmond, IN · On-site

... Google * 22,000+ five star Zillow reviews 1Energage Top Workplaces Awards 2Great Place to Work ... techniques, product and technical requirements. In conjunction with the Branch Manager and ...

Hilltop Holdings

Production Manager, NE

Richmond, IN · On-site

... techniques, product and technical requirements. In conjunction with the Branch Manager and ... Google * 22,000+ five star Zillow reviews 1Energage Top Workplaces Awards 2Great Place to Work ...

Amrize

Director of Codes Engineering

Fishers, IN

High proficiency in generating, analyzing, and presenting metrics using Microsoft and Google productivity suites. * Impeccable focus, time management, and highly effective interpersonal and ...

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All JobsGoogle Product JobsGoogle Product Manager JobsGoogle Product Manager Jobs in Indiana

Google Product Manager information

See Indiana salary details

$49K

$151.7K

$187.5K

How much do google product manager jobs pay per year?

As of Aug 20, 2026, the average yearly pay for google product manager in Indiana is $151,684.00, according to ZipRecruiter salary data. Most workers in this role earn between $134,200.00 and $187,500.00 per year, depending on experience, location, and employer.

What does a Google Product Manager do?

A Google Product Manager (PM) is responsible for guiding the development, launch, and ongoing improvement of Google’s products and services. They work closely with engineering, design, marketing, and business teams to define product vision, strategy, and roadmap. PMs gather user feedback, analyze market trends, and prioritize features to ensure products meet both user needs and business goals. Their role requires strong communication, analytical skills, and the ability to balance technical and business considerations.

How does a Google product manager typically collaborate with engineering and design teams during the product development process?

As a Google Product Manager, you'll work closely with cross-functional teams, especially engineering and design, throughout the product development lifecycle. You'll be responsible for clearly articulating the product vision, prioritizing features, and ensuring alignment on goals during sprint planning and regular check-ins. Effective communication and the ability to balance technical feasibility with user needs are key, as you'll often facilitate discussions, resolve conflicts, and help teams stay focused on delivering impactful solutions. This collaborative environment is fast-paced and dynamic, offering frequent opportunities for learning and professional growth.

What are the key skills and qualifications needed to thrive as a Google Product Manager, and why are they important?

To thrive as a Google Product Manager, you need strong analytical skills, product sense, user empathy, and experience in product development, often supported by a degree in business, engineering, or computer science. Familiarity with tools like SQL, A/B testing platforms, product roadmapping software, and agile frameworks is typically expected. Exceptional communication, leadership, and stakeholder management skills distinguish top performers in this role. These skills are crucial for driving product vision, coordinating cross-functional teams, and delivering impactful products in a fast-paced tech environment.

What is the difference between Google Product Manager vs Google Program Manager?

AspectGoogle Product ManagerGoogle Program Manager
Required credentialsBachelor's degree, often MBA or technical backgroundBachelor's degree, project management certifications beneficial
Work environmentFocus on product development, user experience, and market strategyFocus on managing projects, coordinating teams, and delivering programs
Employer and industry usageCommon in tech companies, especially in product-centric rolesCommon in tech and large organizations managing multiple projects
Search and comparison intentUnderstanding role differences, career paths, responsibilitiesClarifying scope, skills, and career progression

Google Product Managers primarily focus on developing and managing products, emphasizing user needs and market fit. In contrast, Google Program Managers coordinate multiple projects, ensuring timely delivery and cross-team collaboration. Both roles require strong organizational skills, but their core responsibilities differ significantly, making it important to understand these distinctions when exploring career options or job opportunities.

Does Google hire product managers?

Yes, Google hires product managers to lead the development and strategy of its products and services. These roles typically require strong project management skills, technical knowledge, and experience in product lifecycle management. Google often looks for candidates with relevant experience, a track record of successful product delivery, and proficiency in tools like Agile and data analysis.

How do I become a Google Product Manager?

To become a Google Product Manager, candidates typically need a strong background in computer science, engineering, or business, along with experience in product development, project management, or related roles. Developing skills in data analysis, user experience, and cross-functional collaboration is important, and familiarity with tools like Google Workspace or product management software can be beneficial. A bachelor's degree is usually required, and many successful candidates hold advanced degrees or relevant certifications in product management or related fields.

What are popular job titles related to Google Product Manager jobs in Indiana?

For Google Product Manager jobs in Indiana, the most frequently searched job titles are:

What job categories do people searching Google Product Manager jobs in Indiana look for?

The top searched job categories for Google Product Manager jobs in Indiana are:

What cities in Indiana are hiring for Google Product Manager jobs?

Cities in Indiana with the most Google Product Manager job openings:

Google Product Manager jobs near you
Infographic showing various Google Product Manager job openings in Indiana as of August 2026, with employment types broken down into 85% Full Time, 13% Part Time, and 2% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $151,684 per year, or $72.9 per hour.

Google AI Lead Architect

Deloitte

Indianapolis, IN • On-site

$52.75 - $72.50/hr

Full-time

Re-posted 2 days ago

Deloitte rating

8.2

Company rating: 8.2 out of 10

Based on 92 frontline employees who took The Breakroom Quiz

45th of 151 rated financial services

Job description

Google AI Lead Architect/AI & Engineering:

Join our AI & Engineering team in transforming technology platforms, driving innovation, and helping make a significant impact on our clients' success. You'll work alongside talented professionals reimagining and re-engineering operations and processes that are critical to businesses. Your contributions can help clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation.
AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. Our delivery models are tailored to meet each client's unique requirements.
Engineering as a Service provides complete design, implementation, and technology operations, leveraging our core engineering expertise. We transform engineering teams, modernize technology, and deliver complex programs with a product engineering approach. Our flexible delivery models-traditional teams, pools, or pods-are tailored to each client's needs, offering engineering-led advisory, implementation, and operational capabilities to accelerate innovation.

Recruiting for this role ends on 8-31-2026
Work you'll do:

  • Architect and deliver enterprise AI platforms and applications on Google Cloud using Vertex AI and Gemini; optimize for scalability, reliability, security, and cost.
  • Design, fine-tune, evaluate, and govern LLM solutions with Gemini on Vertex AI (prompt/tool/function calling, safety policies, Vector Search, evaluation); implement deployment, inference optimization, and monitoring.
  • Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement context management, retrieval strategies, and observability.
  • Define end-to-end architectures across data pipelines, feature engineering, model lifecycle, APIs/microservices, and CI/CD/MLOps/LLMOps with Vertex AI Pipelines and Cloud Build.
  • Lead cloud-native development on GKE, Cloud Run, Pub/Sub, BigQuery, Cloud SQL/Spanner, Memorystore, and Terraform; enforce application and agentic design patterns.
  • Implement security and governance for AI/ML systems (data privacy, model poisoning, adversarial attacks); apply Gemini safety features and enterprise guardrails.

Responsibilities include:

  • Architect and Design: Lead the design and development of enterprise-grade AI applications and platforms, with a focus on scaling AI solutions for production. This includes defining the technical architecture, selecting appropriate technologies, and ensuring solutions are robust, scalable, and secure.
  • LLM and AI Integration: Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an emphasis on production-level performance and reliability.
  • Enterprise Architecture: Collaborate with enterprise architects to ensure AI solutions align with the broader company's technical strategy, governance, and standards.
  • Cloud and GenAI Native Development: Design and deploy applications using Cloud Native principles on a hyperscaler platform (AWS, Azure, GCP). Leverage a wide range of hyperscaler tools and services, including containers (Docker, Kubernetes), serverless functions, and managed databases. Should have experience in leveraging various GenAI tools to accelerate software development life cycle.
  • Security & Governance: Ensure the security of all AI/ML systems by addressing potential vulnerabilities such as data privacy concerns, model poisoning, and adversarial attacks.
  • Design Patterns: Apply and enforce Application Design Patterns and Agentic Design Patterns to build resilient and maintainable software systems.

 Required Qualifications

  • Bachelor's degree in Computer Science, Engineering or a related technical field.
  • 8+ years' experience as a Software or Solution Architect, with a strong focus on application development and scaling solutions for production environments.
  • 5+ years hands-on with Google Cloud, including 2+ end-to-end enterprise implementations in production.
  • 4+ years designing and implementing Google Cloud networks, security controls, and landing zones using Terraform.
  • 3+ years building and operating containerized workloads on GKE (autoscaling, ingress, monitoring/observability).
  • 3+ years implementing CI/CD and DevSecOps with Cloud Build, GitHub Actions, or Jenkins.
  • 3+ years executing migration or modernization programs to Google Cloud (rehost, replatform, refactor).
  • 2+ years applying AI/GenAI on Google Cloud with Vertex AI and Gemini, including 1+ years' production deployment (e.g. RAG with Vertex AI Search/Vector Search, prompt design, safety policies, observability).
  • Deep understanding of AI/ML concepts, including experience with LLMs and their application in enterprise settings.
  • Experience implementing multiple AI solutions in a professional, real-world environment.
  • Strong understanding of security implications related to AI/ML systems (e.g., data privacy, model poisoning, adversarial attacks).
  • Familiarity with various hyperscaler tools and services.
  • Hyperscaler Architect certification is required (e.g., AWS Certified Solutions Architect, Azure Solutions Architect Expert, or GCP Professional Cloud Architect).
  • Ability to travel up to 50% based on the work you do and the clients and industries/sectors you serve.
  • Limited immigration sponsorship may be available.

Preferred Qualifications:

  • Google Professional Machine Learning Engineer certification or the equivalent ML certification.
  • Master's degree in technology-related discipline.
  •  2+ years's leading high performance, results driven engineering teams delivering AI platforms or applications.
  • 1+ year implementing LLMOps/MLOps using Vertex AI Pipelines and Cloud Build (or similar)

Wages + Salary

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $141,200 to $278,300.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

Qualifications:

Google AI Lead Architect/AI & Engineering:

Join our AI & Engineering team in transforming technology platforms, driving innovation, and helping make a significant impact on our clients' success. You'll work alongside talented professionals reimagining and re-engineering operations and processes that are critical to businesses. Your contributions can help clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation.
AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. Our delivery models are tailored to meet each client's unique requirements.
Engineering as a Service provides complete design, implementation, and technology operations, leveraging our core engineering expertise. We transform engineering teams, modernize technology, and deliver complex programs with a product engineering approach. Our flexible delivery models-traditional teams, pools, or pods-are tailored to each client's needs, offering engineering-led advisory, implementation, and operational capabilities to accelerate innovation.

Recruiting for this role ends on 8-31-2026
Work you'll do:

  • Architect and deliver enterprise AI platforms and applications on Google Cloud using Vertex AI and Gemini; optimize for scalability, reliability, security, and cost.
  • Design, fine-tune, evaluate, and govern LLM solutions with Gemini on Vertex AI (prompt/tool/function calling, safety policies, Vector Search, evaluation); implement deployment, inference optimization, and monitoring.
  • Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement context management, retrieval strategies, and observability.
  • Define end-to-end architectures across data pipelines, feature engineering, model lifecycle, APIs/microservices, and CI/CD/MLOps/LLMOps with Vertex AI Pipelines and Cloud Build.
  • Lead cloud-native development on GKE, Cloud Run, Pub/Sub, BigQuery, Cloud SQL/Spanner, Memorystore, and Terraform; enforce application and agentic design patterns.
  • Implement security and governance for AI/ML systems (data privacy, model poisoning, adversarial attacks); apply Gemini safety features and enterprise guardrails.

Responsibilities include:

  • Architect and Design: Lead the design and development of enterprise-grade AI applications and platforms, with a focus on scaling AI solutions for production. This includes defining the technical architecture, selecting appropriate technologies, and ensuring solutions are robust, scalable, and secure.
  • LLM and AI Integration: Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an emphasis on production-level performance and reliability.
  • Enterprise Architecture: Collaborate with enterprise architects to ensure AI solutions align with the broader company's technical strategy, governance, and standards.
  • Cloud and GenAI Native Development: Design and deploy applications using Cloud Native principles on a hyperscaler platform (AWS, Azure, GCP). Leverage a wide range of hyperscaler tools and services, including containers (Docker, Kubernetes), serverless functions, and managed databases. Should have experience in leveraging various GenAI tools to accelerate software development life cycle.
  • Security & Governance: Ensure the security of all AI/ML systems by addressing potential vulnerabilities such as data privacy concerns, model poisoning, and adversarial attacks.
  • Design Patterns: Apply and enforce Application Design Patterns and Agentic Design Patterns to build resilient and maintainable software systems.

 Required Qualifications

  • Bachelor's degree in Computer Science, Engineering or a related technical field.
  • 8+ years' experience as a Software or Solution Architect, with a strong focus on application development and scaling solutions for production environments.
  • 5+ years hands-on with Google Cloud, including 2+ end-to-end enterprise implementations in production.
  • 4+ years designing and implementing Google Cloud networks, security controls, and landing zones using Terraform.
  • 3+ years building and operating containerized workloads on GKE (autoscaling, ingress, monitoring/observability).
  • 3+ years implementing CI/CD and DevSecOps with Cloud Build, GitHub Actions, or Jenkins.
  • 3+ years executing migration or modernization programs to Google Cloud (rehost, replatform, refactor).
  • 2+ years applying AI/GenAI on Google Cloud with Vertex AI and Gemini, including 1+ years' production deployment (e.g. RAG with Vertex AI Search/Vector Search, prompt design, safety policies, observability).
  • Deep understanding of AI/ML concepts, including experience with LLMs and their application in enterprise settings.
  • Experience implementing multiple AI solutions in a professional, real-world environment.
  • Strong understanding of security implications related to AI/ML systems (e.g., data privacy, model poisoning, adversarial attacks).
  • Familiarity with various hyperscaler tools and services.
  • Hyperscaler Architect certification is required (e.g., AWS Certified Solutions Architect, Azure Solutions Architect Expert, or GCP Professional Cloud Architect).
  • Ability to travel up to 50% based on the work you do and the clients and industries/sectors you serve.
  • Limited immigration sponsorship may be available.

Preferred Qualifications:

  • Google Professional Machine Learning Engineer certification or the equivalent ML certification.
  • Master's degree in technology-related discipline.
  •  2+ years's leading high performance, results driven engineering teams delivering AI platforms or applications.
  • 1+ year implementing LLMOps/MLOps using Vertex AI Pipelines and Cloud Build (or similar)

Wages + Salary

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of...

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