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📍 Job Overview

Job Title: Gemini AI Architect - Governance Control Tower

Company: Inabia Software & Consulting Inc. (End Client: Zensar)

Location: San Ramon, CA

Job Type: CONTRACTOR

Category: AI Architecture / Governance & Control Tower

Date Posted: 2026-08-20

Experience Level: 10+ Years

Remote Status: Hybrid (Minimum 3 days/week onsite required)

🚀 Role Summary

• This is a critical architectural ownership role for an enterprise AI Governance Control Tower, demanding end-to-end design and direct engagement with platform leadership.

• The position requires defining robust architecture standards, implementing checklist criteria for agent deployment, and managing relationships with review boards.

• You will lead a cross-functional delivery pod responsible for the implementation and ongoing operation of the AI Governance Control Tower platform.

• The role involves hands-on work across both the build phase (establishing the Control Tower) and the run phase (transitioning to a support pod while maintaining architectural oversight).

• Key workstreams include Governance & Standards, Registry & Gateway Operations, Connector & Retrieval Engineering, Identity & Entitlement Enforcement, and Observability & Cost Control.

📝 Enhancement Note: This role is highly specialized, focusing on the architectural governance and operational control of enterprise-scale AI systems, particularly those leveraging Google Gemini. The emphasis on "architectural owner" and "platform leadership deals with directly" indicates a senior, strategic position with significant influence and direct accountability. The hybrid work arrangement with a minimum onsite requirement suggests a need for strong collaboration and on-site strategic planning.

📈 Primary Responsibilities

• Design and architect the reusable top-level agent layer, including orchestrator, context engineering, retrieval, synthesis, and response mechanisms, serving as the unified entry point for all platform applications.

• Lead and direct the cross-functional delivery pod, establishing technical vision, conducting work reviews, and ensuring accountability for all platform deliverables.

• Act as the primary liaison between platform leadership and the delivery team, translating strategic directives into actionable architecture and developing defensible implementation plans.

• Uphold stringent quality standards through rigorous evaluation datasets, threshold gates, and regression testing to ensure platform reliability and scalability as agent numbers increase.

• Take ownership of the architecture across all five critical workstreams: Governance & Standards, Registry & Gateway Operations, Connector & Retrieval Engineering, Identity & Entitlement Enforcement, and Observability & Cost Control.

• Define and enforce architecture standards and deployment checklists that all agents must clear before going live.

• Manage the relationship and communication with the AI Governance Review Board.

• Oversee the implementation and operationalization of the Governance Control Tower, ensuring smooth transition from build to run phases.

📝 Enhancement Note: The responsibilities clearly delineate a leadership role with both strategic architectural design and tactical execution oversight. The emphasis on "holding the quality bar" and "making standards stick" points to a need for strong governance and enforcement capabilities.

🎓 Skills & Qualifications

Education:

• While no specific degree is mentioned, a Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field is typically expected for this level of architectural responsibility. Experience:

• 10+ years of comprehensive experience in software engineering and architecture.

• Minimum of 3 years of direct experience designing, building, and running applied AI systems in a production environment.

• Proven track record as the architectural owner of an enterprise-level platform, successfully defining and enforcing standards across multiple teams.

• Demonstrated experience with multi-agent systems in a production setting, including orchestration, routing, tool utilization, memory management, and human-in-the-loop integration, with evidence of operational ownership. Required Skills:

• Hands-on expertise with Google Gemini Enterprise and Google ADK, or a directly comparable enterprise agent platform.

• Deep understanding of runtime environments, agent registration, identity management, and observability within AI platforms.

• Strong grounding in Retrieval Augmented Generation (RAG) and retrieval architecture, including vector stores, embedding models, chunking strategies, and hybrid search techniques.

• Proficiency in Identity and Access Management (IAM) concepts, including OAuth2, SAML, RBAC, token exchange, propagation of service-account vs. end-user credentials, and mapping document-level ACLs to retrieval layers.

• Proficiency in Python; comfortable with Go or an equivalent second programming language.

• Experience with MCP (likely referring to a specific AI/ML platform component or framework), including building servers, not just consuming them.

• Solid understanding of cloud-native principles and systems fundamentals, with a strong preference for Google Cloud Platform (GCP) services such as Cloud Run, GKE, Vertex AI, networking, and IAM.

• Awareness of cost management at scale, including token and inference spend across a growing agent estate (FinOps). Preferred Skills:

• Experience in AI Governance and establishing control frameworks for AI deployments.

• Familiarity with Agent Registry and Gateway Operations.

• Experience with Observability and FinOps specific to AI/ML workloads.

📝 Enhancement Note: The "Must-Haves" list is extensive and highly specific, indicating a very niche and senior technical requirement. Candidates should be prepared to demonstrate deep, practical expertise in each of these areas. The "preferred skills" suggest a desire for someone who can also contribute to the broader operational and financial aspects of AI deployment.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate architectural designs for complex AI platforms or governance frameworks, showcasing end-to-end ownership and scalability considerations.

• Present case studies of enterprise AI systems you have architected and managed in production, highlighting challenges, solutions, and operational outcomes.

• Include examples of defined architecture standards, quality gates, and deployment checklists implemented for AI systems.

• Showcase experience with implementing and operating AI governance controls, including identity and entitlement enforcement mechanisms.

• Provide evidence of contributions to building or operating core AI platform components (e.g., orchestrators, retrieval systems, registries). Process Documentation:

• Documented workflows for the design and implementation of AI governance frameworks.

• Process maps detailing the operationalization of AI platforms, including monitoring, cost control, and incident response.

• Examples of how you have established and maintained quality assurance processes for AI model deployments and agent lifecycle management.

• Demonstrate a systematic approach to translating business requirements into technical architecture and actionable delivery plans.

📝 Enhancement Note: For a role of this seniority and specialization, a portfolio should not just list projects but tell a story of architectural leadership, problem-solving, and impact. Emphasis should be placed on how standards were set, enforced, and how reliability and cost-efficiency were managed at scale.

💵 Compensation & Benefits

Salary Range:

• Based on the provided rate of $55/hr for a W-2 contractor (through Inabia), this translates to an annual gross income of approximately $114,400 for a standard 40-hour work week.

• This rate is competitive for senior contract roles in the San Ramon, CA area, particularly for highly specialized AI architecture positions. It aligns with market rates for individuals with 10+ years of experience and deep expertise in enterprise AI platforms and governance. Benefits:

• As a contractor through Inabia Software & Consulting Inc., benefits would typically be provided by Inabia. These may include:

  • Health, Dental, and Vision Insurance (details vary by contract and provider)

  • Paid Time Off (PTO) or Vacation Days (details vary)

  • Retirement Savings Plan (e.g., 401(k), details vary)

  • Potential for Professional Development or Training Reimbursement

  • Life Insurance and Disability Coverage Working Hours:

• Standard full-time working hours are assumed to be 40 hours per week.

• Flexibility may be available, but the requirement for a minimum of 3 days onsite in San Ramon, CA, indicates a structured work week with clear in-office expectations.

📝 Enhancement Note: The salary is specified as an hourly rate for a W-2 contractor. This is crucial information for candidates to understand their take-home pay and the benefits structure. The annual equivalent provides a clearer picture for long-term financial planning.

🎯 Team & Company Context

🏢 Company Culture

Industry: Software & Consulting (Inabia), Technology Services & Consulting (Zensar - End Client). Inabia specializes in providing IT staffing and consulting services, while Zensar is a global technology services and consulting company.

Company Size: Inabia Software & Consulting Inc. is likely a medium-sized staffing and consulting firm. Zensar is a large enterprise, with tens of thousands of employees globally.

Founded: Inabia's founding date is not provided, but its presence on platforms like this suggests it's an established player. Zensar was founded in 1991.

Team Structure:

• Delivery Pod: The role involves leading a "cross-functional delivery pod." This implies a team composed of engineers, potentially product managers, QA specialists, and other roles essential for building and operating the AI platform.

• Reporting: The "architectural owner" directly engages with "platform leadership." This suggests a reporting line that is likely senior within the technology or product organization, potentially to a VP of Engineering, CTO, or Head of AI/Platform.

• Collaboration: Strong cross-functional collaboration is essential, involving interaction with platform leadership, the delivery pod, potentially other engineering teams, and governance stakeholders.

Methodology:

• AI Governance: The core of the role is establishing and enforcing AI governance standards, ensuring responsible and controlled deployment of AI agents.

• Platform Engineering: Hands-on involvement in building and operating a complex, cloud-native AI platform.

• Agile/DevOps: Leading a delivery pod and implementing/operating a platform suggests adherence to agile methodologies and DevOps practices for continuous integration and deployment.

• Data-Driven Decision Making: The role requires defining evaluation datasets and thresholds, indicating a data-centric approach to quality assurance and platform performance.

Company Website: https://www.inabia.com/ (for Inabia), https://www.zensar.com/ (for Zensar, the end client)

📝 Enhancement Note: Understanding the relationship between Inabia (the staffing agency) and Zensar (the end client) is crucial. This role is for Inabia's W-2 contractors to work on Zensar's project. The "company culture" described here largely reflects the environment at Zensar, the end client, where the work will be performed.

📈 Career & Growth Analysis

Operations Career Level: This is a Principal or Lead Architect level role within the AI/Platform Engineering domain. It represents a senior individual contributor (IC) path with significant technical leadership and strategic influence, rather than a management track.

Reporting Structure: The role reports directly to high-level platform leadership, indicating a position of significant visibility and influence within the organization. The individual will also lead a dedicated delivery pod.

Operations Impact: The "Governance Control Tower" is central to the safe, compliant, and efficient deployment and operation of AI across the enterprise. The impact of this role is directly tied to mitigating risks, ensuring compliance, controlling costs, and enabling the scalable adoption of AI technologies. Success in this role directly enables and safeguards the organization's AI strategy.

Growth Opportunities:

• Deep Specialization: Opportunity to become a recognized expert in enterprise AI governance, Gemini architecture, and multi-agent systems, a highly in-demand skill set.

• Strategic Influence: Direct engagement with platform leadership provides opportunities to shape the future direction of AI strategy and implementation within Zensar.

• Leadership: Lead a dedicated cross-functional pod, gaining experience in managing technical teams and project delivery in a complex AI environment.

• Broader Platform Ownership: Potential to expand architectural ownership to other critical components of Zensar's AI ecosystem.

• Industry Recognition: Contributing to a high-profile AI governance initiative could lead to speaking engagements, publications, or industry recognition.

📝 Enhancement Note: This role offers a significant opportunity for a senior architect to make a substantial impact on a critical, cutting-edge initiative within a large enterprise. The growth path is focused on deepening technical expertise and strategic influence rather than traditional people management.

🌐 Work Environment

Office Type: The requirement for "Minimum 3 days/week onsite" in San Ramon, CA, indicates a hybrid work model within a corporate office environment. This suggests a professional setting designed for collaboration, meetings, and focused work.

Office Location(s): San Ramon, California. This is a business hub in the San Francisco Bay Area, offering accessibility to talent and resources.

Workspace Context:

• Collaborative Environment: The hybrid model and leadership role imply a need for collaborative work sessions, whiteboard strategy sessions, and direct interaction with the delivery pod and platform leadership.

• Technology & Tools: Access to enterprise-grade cloud infrastructure (GCP), AI development tools, collaboration software, and potentially specialized AI governance platforms will be essential.

• Team Interaction: Regular interaction with a dedicated delivery pod, platform leadership, and potentially other engineering and compliance teams.

Work Schedule: While the core work is likely 40 hours per week, the nature of architectural ownership for a production platform may require some flexibility for critical incidents or deployment windows. The hybrid component requires adherence to specific in-office days.

📝 Enhancement Note: The hybrid nature of the role means candidates should be comfortable working both independently and collaboratively, with a clear expectation of in-office presence for strategic discussions and team engagement.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Likely a recruiter screen by Inabia to assess basic qualifications, experience level, and alignment with contract terms (rate, work arrangement).

• Technical Screen: A conversation with a hiring manager or senior technical lead from Zensar to dive deep into AI architecture, Gemini, RAG, Python, cloud-native concepts, and IAM.

• Architectural Deep Dive / Case Study: A comprehensive session focusing on your experience designing and owning enterprise platforms. Expect detailed questions about how you've set standards, managed quality, handled identity/entitlements, and dealt with cost control. You might be asked to walk through a past project or a hypothetical scenario related to the Control Tower.

• Leadership / Cross-functional Interview: An interview with platform leadership to assess strategic thinking, communication skills, ability to translate technical concepts to business stakeholders, and cultural fit.

• Reference Checks: Standard verification of past employment and performance.

Portfolio Review Tips:

• Focus on Impact: For each project, clearly articulate the problem, your architectural solution, the specific technologies used (Gemini, RAG, Python, GCP, etc.), the quality/governance standards you implemented, and the measurable outcomes (reliability, cost savings, compliance improvements).

• Showcase Ownership: Highlight instances where you were the sole architectural owner, set standards that others followed, and managed the end-to-end lifecycle of a platform.

• Quantify Achievements: Use numbers whenever possible. For example, "Reduced agent deployment errors by X% by implementing Y standard," or "Managed inference costs for Z agents, achieving Y% cost savings through optimization."

• Structure for Clarity: Organize your portfolio by key responsibilities or technologies. Be prepared to present a "top 1-2" projects in detail during interviews.

• Demonstrate Governance Focus: Explicitly show how you've addressed AI governance, identity, entitlements, and observability in your past work.

Challenge Preparation:

• Architectural Design Challenge: Be ready to outline the architecture for the Governance Control Tower, or a significant component of it, under time pressure. Focus on scalability, security, reliability, and cost-efficiency.

• Problem-Solving Scenario: Prepare to discuss how you would handle a specific governance violation, a performance issue in the retrieval layer, or a cost overrun related to agent usage.

• Technical Deep Dive: Brush up on advanced Python, Go, GCP services (GKE, Cloud Run, Vertex AI), IAM protocols (OAuth2, SAML), and RAG best practices.

📝 Enhancement Note: The interview process will heavily scrutinize your ability to not only design complex AI systems but also to govern them effectively and manage their operational lifecycle at an enterprise scale. Your portfolio is your primary tool to demonstrate this.

🛠 Tools & Technology Stack

Primary Tools:

• Google Gemini Enterprise / Google ADK: Core platform for building and managing AI agents.

• Python: Primary programming language for development and scripting.

• Go: Secondary language proficiency is a plus.

• MCP (AI Platform Component): Experience in building servers, not just consuming them.

• GCP Services:

  • Cloud Run / GKE: Container orchestration and serverless compute.
  • Vertex AI: Managed ML platform for training and deployment.
  • Cloud IAM: Identity and Access Management for GCP resources.
  • Networking: Understanding of GCP networking constructs.

Analytics & Reporting:

• Observability Tools: For monitoring AI system performance, logs, and metrics (specific tools not mentioned but crucial for operations).

• Cost Management Tools: Within GCP for monitoring and optimizing inference and token spend (FinOps).

• Data Analysis Tools: To analyze evaluation datasets and performance metrics.

CRM & Automation:

• While not directly a CRM role, understanding how AI agents interact with downstream systems or data sources is key.

• Automation Tools: For CI/CD pipelines, infrastructure as code (IaC), and workflow orchestration within GCP.

Other Relevant Technologies:

• Vector Stores: For RAG implementation (e.g., Pinecone, Weaviate, Google Vector Search).

• Embedding Models: Understanding of various models and their applications.

• Identity & Access Protocols: OAuth2, SAML, RBAC.

📝 Enhancement Note: Deep familiarity with the Google Cloud Platform ecosystem is paramount, given the preference for GCP. Proficiency in the specific AI development and governance tools mentioned (Gemini, ADK, MCP) is non-negotiable.

👥 Team Culture & Values

Operations Values:

• Reliability & Stability: A strong emphasis on ensuring the AI platform is dependable and performs consistently, driven by robust architecture and quality control.

• Governance & Compliance: Upholding strict standards for AI deployment, ensuring ethical use, security, and regulatory adherence.

• Efficiency & Cost Optimization: A focus on managing resource utilization and spend effectively, particularly for inference and token consumption.

• Innovation with Control: Encouraging the adoption of new AI capabilities while maintaining a strong governance framework.

• Collaboration & Ownership: Fostering a team environment where individuals take ownership of their domains and collaborate effectively to achieve shared goals.

Collaboration Style:

• Cross-Functional Integration: Expect to work closely with product leadership, other engineering teams, security, legal, and compliance departments.

• Architectural Guidance: A style that involves providing clear technical direction, setting standards, and ensuring alignment across different engineering efforts.

• Feedback Loops: Establishing mechanisms for feedback on deployed agents and platform performance to drive continuous improvement.

• Knowledge Sharing: Promoting a culture where insights on AI best practices, governance challenges, and operational efficiencies are shared across teams.

📝 Enhancement Note: The culture will likely be a blend of a fast-paced, innovative AI development environment and a highly controlled, regulated operational setting due to the governance focus. Candidates should demonstrate an ability to thrive in both.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapidly Evolving AI Landscape: Keeping pace with the continuous advancements in AI models, frameworks, and best practices while maintaining stable governance.

• Balancing Innovation and Control: Finding the optimal balance between enabling rapid AI adoption and enforcing strict governance and quality standards.

• Complexity of Multi-Agent Systems: Architecting and managing the intricate interactions, orchestration, and dependencies within large-scale multi-agent systems.

• Scalability of Governance: Ensuring that governance processes and controls scale effectively as the number of AI agents and use cases grows exponentially.

• Cost Management at Scale: Effectively monitoring and controlling the significant inference and token costs associated with enterprise AI deployments.

Learning & Development Opportunities:

• Deep Dive into Google AI Ecosystem: Extensive hands-on experience with Google Gemini Enterprise, ADK, and other GCP AI services.

• Expertise in AI Governance: Becoming a leading authority on enterprise AI governance frameworks and best practices.

• Advanced RAG and Multi-Agent Systems: Sharpening skills in complex AI system design and operationalization.

• Cloud-Native Architecture: Further developing expertise in designing and operating resilient, scalable systems on GCP.

• FinOps for AI: Gaining specialized knowledge in managing and optimizing AI-related cloud spend.

📝 Enhancement Note: This role presents significant technical challenges that are at the forefront of AI adoption in large enterprises. Overcoming these challenges will provide unparalleled growth and expertise.

💡 Interview Preparation

Strategy Questions:

• "Describe your experience as an architectural owner of an enterprise platform. How did you define and enforce standards, and what was the outcome?"

• "Walk us through a complex multi-agent system you designed or operated. What were the key orchestration and retrieval challenges, and how did you address them?"

• "How would you design the Governance Control Tower's architecture to ensure scalability, security, and compliance for hundreds of AI agents?"

• "Detail your approach to implementing identity and entitlement enforcement for AI agents accessing sensitive data. What protocols and mapping strategies would you use?"

• "What are your strategies for balancing rapid AI innovation with stringent quality gates and operational reliability?" Company & Culture Questions:

• "What do you know about Zensar's AI initiatives and how does this role fit into their broader strategy?"

• "How do you approach FinOps and cost management for large-scale AI deployments?"

• "Describe a time you had to gain buy-in from senior leadership for a technical architectural decision. How did you present your case?"

• "How do you foster collaboration within a cross-functional delivery pod, especially when working on a hybrid model?" Portfolio Presentation Strategy:

• Focus on the "Why": For each case study, clearly articulate the business problem and the strategic importance of your architectural solution.

• Highlight Governance: Explicitly call out how your projects addressed AI governance, data security, identity management, and operational control.

• Technical Depth: Be prepared to dive deep into the technical specifics of your architecture, RAG implementation, Python code structure, and GCP configurations.

• Quantify Impact: Present metrics on performance, reliability, cost savings, and compliance improvements achieved through your architectural designs.

• Storytelling: Frame your experience as a narrative of leadership, problem-solving, and driving impactful results in complex AI environments.

📝 Enhancement Note: Be ready to discuss your experience with Google Gemini Enterprise and ADK extensively. If you have direct experience with MCP, highlight it. Candidates should prepare to defend their architectural choices and demonstrate a deep understanding of enterprise-level AI operational challenges.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided application link on JazzHR.

• Customize Your Resume: Tailor your resume to prominently feature keywords and experience related to Google Gemini Enterprise, Google ADK, Python, RAG, AI Governance, Identity & Entitlements, Observability, FinOps, GCP, and enterprise-scale AI architecture. Quantify achievements wherever possible.

• Prepare Your Portfolio: Curate a selection of your most relevant projects that showcase your experience in architectural ownership, AI system design, governance implementation, and operational management. Be ready to present 1-2 key projects in detail during interviews.

• Research Zensar & Inabia: Understand Zensar's business, their focus on AI, and Inabia's role as a consulting partner. Familiarize yourself with their company values and recent news.

• Practice Technical Deep Dives: Review core concepts in Python, Go, GCP services, IAM protocols, and RAG architecture. Be prepared for in-depth technical questions and potential architectural design scenarios.

• Formulate Strategy Answers: Prepare thoughtful responses to strategic questions about platform ownership, governance, and leadership, drawing from your portfolio and experience.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization (Inabia Software & Consulting Inc.) before making application decisions. This role is a contractor position through Inabia, working as an end client for Zensar.