Google Deployment Engineer Jobs Near Me

Google AI Architect/AI and 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 10-31-2026
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: 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.
• 6+ 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.
• 2+ years building and operating containerized workloads on GKE (autoscaling, ingress, monitoring/observability).
• 2+ 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 $122,000-$240,500.

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.

Information for applicants with a need for accommodation: 

https://www2.deloitte.com/us/en/pages/careers/articles/join-deloitte-assistance-for-disabled-applicants.html

Google AI Architect/AI and 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 10-31-2026
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: 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.
• 6+ 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.
• 2+ years building and operating containerized workloads on GKE (autoscaling, ingress, monitoring/observability).
• 2+ 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 ind...