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Program Manager Google Cloud Ai Jobs (NOW HIRING)

Google

Customer Engineer, Platform, Google Cloud

Sunnyvale, CA · On-site

$65.50 - $87.50/hr

... management, data analytics, cloud AI, networking, migrations, security. About the job The Google ... Cloud Platform team helps customers transform and build what's next for their business - all with ...

Google

GECX Sales Specialist, Google Cloud

Austin, TX · On-site

Experience identifying AI use cases to solve customer issues or selling customer experience (e.g ... Experience working with and managing partners in implementation projects, including global system ...

Credence company

Senior Google AI Engineer

Mclean, VA

$107K - $147K/yr

... Cloud-accelerating mission outcomes for a high visibility program. As a Senior Google AI Engineer ... management-aligned to NIST 80053, RMF, and FedRAMP baselines. * Operationalize LLM/GenAI (RAG ...

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Program Manager Google Cloud Ai information

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How much do program manager google cloud ai jobs pay per year?

As of Jun 9, 2026, the average yearly pay for program manager google cloud ai in the United States is $107,460.00, according to ZipRecruiter salary data. Most workers in this role earn between $79,500.00 and $132,500.00 per year, depending on experience, location, and employer.

What does a Program Manager do at Google Cloud AI?

A Program Manager at Google Cloud AI is responsible for coordinating and driving complex projects that involve artificial intelligence products and services. They work closely with cross-functional teams, including engineers, product managers, data scientists, and external partners, to ensure projects are delivered on time and meet business objectives. Their role involves strategic planning, project scheduling, stakeholder management, risk assessment, and process improvement. They play a critical part in translating business needs into actionable project plans and ensuring the successful launch and adoption of AI solutions within Google Cloud.

How does a Program Manager for Google Cloud AI typically collaborate with engineering and product teams?

As a Program Manager for Google Cloud AI, you will frequently act as a bridge between engineering, product management, and other cross-functional teams. You’ll coordinate project timelines, ensure alignment on goals, and facilitate clear communication to address technical challenges and resource needs. This collaborative approach helps to keep projects on track and ensures that AI solutions are delivered efficiently and meet client expectations. Regular meetings, status updates, and proactive issue resolution are key elements of this dynamic teamwork.

What are the key skills and qualifications needed to thrive as a Program Manager for Google Cloud AI, and why are they important?

To excel as a Program Manager for Google Cloud AI, you need a strong background in project management, cloud technologies, and AI concepts, typically supported by a degree in computer science or a related field. Familiarity with tools like Google Cloud Platform (GCP), Agile methodologies, and certifications such as PMP or Scrum Master are highly beneficial. Outstanding communication, leadership, and stakeholder management skills are essential for coordinating cross-functional teams and driving complex initiatives. These competencies ensure successful project delivery, alignment with business objectives, and effective collaboration in a rapidly evolving tech environment.

Google Cloud Platform AI Engineer

Reliancesoft Systems Inc

Irving, TX

Other

Posted 7 days ago

Job description

Role : Google Cloud Platform AI Engineer

Location : Irving, TX, or Charlotte (100% Onsite)

Hire type: Contract

No of roles : 15

Year of Experience : 8+ yrs

Job Summary:
We are seeking an innovative and highly skilled AI Engineer to join our dynamic team. The ideal candidate will bridge the gap between traditional software engineering and cutting-edge artificial intelligence. You will be instrumental in designing, building, and deploying advanced AI agents, working closely with Large Language Models (LLMs), and driving automated code generation initiatives. If you have a strong foundation in Java and Python, coupled with hands-on experience using Google''s AI tools, we want you to help us build the next generation of intelligent applications

Key Responsibilities:

·       AI Agent Development: Design, build, and deploy autonomous AI agents capable of reasoning, planning, and executing complex workflows.

·       LLM Integration: Integrate cutting-edge Large Language Models (LLMs) into our core products and services to enhance functionality and user experience.

·       Model Context Protocol (MCP) Implementation: Utilize the Model Context Protocol (MCP) to securely connect our AI models to various data sources, tools, and development environments.

·       Automated Code Generation: Leverage AI and LLMs to build systems that assist in, or fully automate, code generation, testing, and optimization processes.

·       System Engineering: Write clean, scalable, and maintainable code in both Java and Python to support AI backend infrastructure.

·       Google Ecosystem Integration: Utilize Google ADK (AI Developer Kits) and related Google Cloud AI services (e.g., Vertex AI, Gemini APIs) to deploy robust AI solutions.

·       Cross-Functional Collaboration: Work closely with product managers, data scientists, and frontend engineers to translate business requirements into technical AI solutions.

Must-Have Qualifications:

·       Programming Languages: Strong proficiency in both Java and Python, with a proven track record of building production-grade software.

·       Google AI Tools: Hands-on experience with Google ADK (or equivalent Google Cloud AI/Vertex AI tools).

·       LLM Expertise: Deep comfort level and practical experience working with Large Language Models (prompt engineering, fine-tuning, RAG architectures).

·       Agentic Workflows: Demonstrable experience in building and orchestrating AI Agents (using frameworks like LangChain, LangGraph, or custom implementations).

·       MCP Knowledge: Familiarity and practical experience with the Model Context Protocol (MCP) for standardizing AI interactions with external tools.

·       Code Generation: Experience in leveraging AI tools or building pipelines specifically for code generation and software automation.

Good-to-Have (Optional but highly valued):

·       Experience with modern robust backend frameworks (e.g., Spring Boot for Java, FastAPI for Python).

·       Familiarity with containerization and orchestration (Docker, Kubernetes).

·       Experience with vector databases (e.g., Pinecone, Weaviate, Milvus).

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