UX Designer | NO C2C

📍 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: Technology / AI Architecture / Governance

Date Posted: August 20, 2026

Experience Level: 10+ Years

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

🚀 Role Summary

• This is a critical architectural leadership role for a client's Gemini AI Governance Control Tower, requiring end-to-end design ownership and direct engagement with platform leadership.

• The position involves defining architecture standards, establishing review board processes, and leading a cross-functional delivery pod responsible for both implementation and ongoing operation of the AI platform.

• The role demands hands-on involvement in a build phase to establish the Control Tower and a run phase to transition to a support team while retaining architectural oversight.

• Key focus areas include Governance & Standards, Registry & Gateway Operations, Connector & Retrieval Engineering, Identity & Entitlement Enforcement, and Observability & Cost Control within an AI ecosystem.

📝 Enhancement Note: This role is highly specialized, focusing on the governance and architectural backbone of an enterprise-grade AI platform, specifically leveraging Google Gemini Enterprise. It requires a blend of deep technical architecture expertise and strong leadership/management capabilities to drive complex AI initiatives from conception to operational maturity. The "NO C2C" designation signifies a direct contractor engagement, likely implying a preference for candidates who can operate with independence and a clear focus on the client's objectives.

📈 Primary Responsibilities

• Design and architect the reusable top-level agent layer, serving as the common entry point for all applications interacting with the AI platform.

• Lead and direct a cross-functional delivery pod, setting technical direction, reviewing team output, and ensuring accountability for delivered solutions.

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

• Establish and maintain a high quality bar for the AI platform through rigorous evaluation datasets, threshold gates, and comprehensive regression testing to ensure reliability as agent count scales.

• Own and manage the architecture across all five core workstreams: Governance & Standards, Registry & Gateway Operations, Connector & Retrieval Engineering, Identity & Entitlement Enforcement, and Observability & Cost Control.

• Develop and enforce architecture standards and checklists that all agents must adhere to before deployment onto the platform.

• Manage the relationship with the AI platform review board, ensuring compliance and facilitating necessary approvals.

• Oversee the implementation and operational transition of the AI Control Tower, ensuring a smooth handover to a right-sized support team.

📝 Enhancement Note: The emphasis on "architectural owner" and "direct platform leadership deals with" highlights the senior and strategic nature of this role. It's not just about designing; it's about owning the vision, the standards, and the execution, with significant influence and direct accountability to executive stakeholders. The responsibility for both build and run phases indicates a need for a candidate comfortable with the full lifecycle of a complex platform.

🎓 Skills & Qualifications

Education:

• While not explicitly stated, a Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field is typically expected for roles requiring 10+ years of software engineering and architecture experience. Experience:

• 10+ years in software engineering and architecture.

• Minimum of 3 years of hands-on experience designing and managing applied AI systems in a production environment.

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

• Demonstrated experience with multi-agent systems in production, including orchestration, routing, tool use, memory management, and human-in-the-loop workflows, with a history of operational ownership.

• Solid experience in building and operating complex systems, including server-side development (e.g., experience with MCP – building servers).

• Experience managing cloud-native infrastructure and systems, with a strong preference for Google Cloud Platform (GCP).

• Experience with cost management and optimization for AI workloads at scale, including token and inference spend. Required Skills:

• Hands-on expertise with Google Gemini Enterprise and its associated development kit (ADK), or a directly comparable enterprise agent platform, covering runtime, registration, identity, and observability components.

• Deep understanding and practical application of Retrieval Augmented Generation (RAG) and retrieval architecture, including vector stores, embedding models, chunking strategies, and hybrid search methodologies.

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

• Strong grounding in Identity and Access Management (IAM) principles and technologies, including OAuth2, SAML, Role-Based Access Control (RBAC), token exchange, service-account vs. end-user credential propagation, and mapping document-level Access Control Lists (ACLs) into a retrieval layer.

• Cloud-native fundamentals and systems architecture, with a strong preference for GCP services such as Cloud Run, Google Kubernetes Engine (GKE), Vertex AI, networking concepts, and GCP IAM.

• Experience with agent orchestration and management platforms (e.g., MCP).

• Awareness and experience in managing operational costs at scale, particularly token and inference spend across a growing agent estate. Preferred Skills:

• Experience with AI Governance frameworks and best practices.

• Familiarity with Observability and FinOps principles applied to AI/ML platforms.

• Experience with Kubernetes (GKE).

• Knowledge of advanced AI concepts beyond RAG, such as complex agentic workflows or meta-learning.

📝 Enhancement Note: The requirement for "architectural owner of an enterprise platform" and "set standards other teams had to follow, and made them stick" points to a candidate who is not only technically adept but also possesses strong influence and change management skills. The specific mention of GCP services indicates a preference for candidates with that cloud ecosystem experience.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate a history of designing and owning complex enterprise platforms, showcasing architectural blueprints, standards documentation, and implementation roadmaps.

• Provide case studies or examples of successfully implemented applied AI systems in production, detailing the architecture, challenges, and outcomes.

• Showcase experience with AI governance frameworks, including how standards were defined, implemented, and enforced.

• Include examples of managing cross-functional delivery pods or technical teams, highlighting leadership, technical direction, and accountability.

• Present evidence of hands-on work with Google Gemini Enterprise, ADK, or comparable enterprise agent platforms, detailing specific features utilized and their impact. Process Documentation:

• Examples of architecture standards and checklists developed for AI agent deployment.

• Documentation illustrating the design of reusable agent layers, orchestration mechanisms, and context engineering frameworks.

• Process flows and documentation for managing AI platform operations, including registry operations, gateway management, and retrieval engineering.

• Evidence of implementing and maintaining identity and entitlement enforcement mechanisms, including RBAC and ACL mapping.

• Documentation related to observability and cost control strategies for AI workloads, detailing monitoring, logging, and FinOps practices.

📝 Enhancement Note: For this role, a portfolio should strongly emphasize architectural leadership, strategic thinking, and the ability to translate complex technical requirements into operational realities. The candidate must be able to articulate how they have driven adoption of standards and ensured the reliability and scalability of enterprise-level AI systems.

💵 Compensation & Benefits

Salary Range:

• Estimated range: $55 - $75 USD per hour (based on $55/hr stated for W-2).

• This estimate reflects the high-level expertise required for an AI Architect role with significant enterprise platform ownership and hands-on Gemini Enterprise experience, combined with the specified location in the San Francisco Bay Area, a high cost-of-living region. The provided rate of $55/hr (Inabia W-2) serves as the baseline. The upper end of the range accounts for variations based on specific experience, interview performance, and the client's final negotiation. Benefits:

• As a contractor through Inabia Software & Consulting Inc., benefits may include:

  • Health, Dental, and Vision Insurance (details to be confirmed with Inabia).

  • 401(k) or similar retirement savings plan options (details to be confirmed).

  • Paid Time Off (PTO) accrual or holiday pay (details to be confirmed).

  • Direct employment with Inabia, offering a stable W-2 relationship.

  • Access to Inabia's internal training and development resources. Working Hours:

• Standard full-time work week, estimated at 40 hours per week.

• The role requires a minimum of 3 days per week onsite in San Ramon, CA, indicating a hybrid work arrangement.

• Flexibility may be available for remote work on non-office days, subject to project needs and team coordination.

📝 Enhancement Note: The stated rate of $55/hr is for an Inabia W-2 employee. This typically implies Inabia offers benefits and handles payroll taxes, while a C2C rate would generally be higher to account for the contractor managing their own benefits and business overhead. The San Ramon location in California suggests competitive compensation due to the high cost of living and strong tech market.

🎯 Team & Company Context

🏢 Company Culture

Industry:

• Inabia Software & Consulting Inc. operates within the IT Services and Consulting industry, specializing in providing technology solutions and staffing for clients.

• The end client, Zensar, is a prominent player in digital solutions and technology services, operating globally. This role is embedded within Zensar's client engagements.

• The context is a high-tech environment focused on AI, cloud computing, and enterprise-level system architecture, implying a culture of innovation, technical excellence, and client-centric delivery. Company Size:

• Inabia Software & Consulting Inc. is a medium-sized company, typically employing between 51-200 professionals, focused on specialized IT consulting and staffing.

• Zensar Technologies is a large, publicly traded global company with thousands of employees worldwide, indicating a robust corporate structure and a wide array of resources. Founded:

• Inabia Software & Consulting Inc. was founded in 2012.

• Zensar Technologies was founded in 1991.

• The long history of both companies suggests stability and established operational practices within their respective domains. Team Structure:

• The candidate will lead a "delivery pod," which is a cross-functional team typically comprising engineers, developers, and potentially AI specialists, all focused on implementing and operating the AI Control Tower.

• This role reports directly to platform leadership within the client (Zensar's client), indicating a high level of visibility and strategic importance.

• Collaboration will be extensive, involving internal Inabia teams for HR/payroll and client-side stakeholders at Zensar and its end-client for project delivery and technical alignment. Methodology:

• The role emphasizes a structured approach to AI architecture and governance, requiring adherence to defined standards and rigorous quality control.

• Agile methodologies are likely employed within the delivery pod for iterative development and implementation.

• Data-driven decision-making is crucial, especially concerning platform reliability, cost control, and performance metrics.

• A strong focus on architectural ownership means driving consensus and ensuring alignment across technical and business stakeholders. Company Website:

• Inabia Software & Consulting Inc.: https://www.inabia.com/

• Zensar Technologies: https://www.zensar.com/

📝 Enhancement Note: The dual context of working for a consulting firm (Inabia) while embedded within a large client engagement (Zensar's client) means the candidate must be adaptable, client-focused, and capable of navigating different organizational cultures and processes. The "Gemini AI Architect" title within a "Governance Control Tower" for an end-client suggests a significant, high-impact project.

📈 Career & Growth Analysis

Operations Career Level:

• This role represents a senior-level architect position, focusing on the strategic design, implementation, and operationalization of a critical AI governance platform. It's a specialized role within AI Architecture, emphasizing governance, control, and enterprise-scale deployment. The candidate is expected to be a subject matter expert and a key technical leader. Reporting Structure:

• The "architectural owner" directly engages with platform leadership, implying a direct reporting line or very close collaboration with senior executives or VPs overseeing AI initiatives.

• The candidate will lead a delivery pod, managing the day-to-day technical execution of the team. Operations Impact:

• This role has a profound impact on the client's ability to safely, reliably, and cost-effectively deploy and scale AI applications.

• By establishing robust governance, standards, and control mechanisms, the architect ensures compliance, mitigates risks, and optimizes resource utilization, directly contributing to the client's AI strategy and ROI.

• Effective management of the Governance Control Tower ensures that AI initiatives align with business objectives and regulatory requirements. Growth Opportunities:

• Technical Specialization: Deepen expertise in enterprise AI governance, multi-agent systems, and advanced RAG architectures.

• Leadership Expansion: Potentially move into broader AI platform leadership roles, managing larger teams or multiple AI initiatives.

• Client Engagement: Develop strong client relationship management skills, leading to further high-impact consulting engagements.

• Cross-functional Exposure: Gain exposure to various business units and their AI adoption strategies, broadening understanding of enterprise AI applications.

• Industry Recognition: Contribute to defining best practices in AI governance, potentially leading to speaking engagements or industry publications.

📝 Enhancement Note: This is a career-defining role for an AI architect, offering significant ownership and the chance to shape a critical enterprise platform. The growth potential lies in deepening specialization, expanding leadership scope, and influencing strategic AI direction within large organizations.

🌐 Work Environment

Office Type:

• The role requires a minimum of 3 days per week onsite in San Ramon, CA, indicating a hybrid work environment. This suggests a modern office setting designed for collaboration, with dedicated workspaces and meeting facilities. Office Location(s):

• San Ramon, California, USA. This location is in the San Francisco Bay Area, a major hub for technology and innovation, offering access to a vibrant tech community. Workspace Context:

• The onsite requirement suggests a collaborative office space where the architect will interact with the delivery pod and other stakeholders.

• Access to necessary technology, high-speed internet, and potentially secure development environments will be provided.

• The environment likely fosters a blend of focused individual work and team collaboration, essential for architectural design and problem-solving.

• The hybrid model allows for focused work from home on non-office days, balancing deep work with team interaction. Work Schedule:

• Standard full-time hours (approximately 40 hours per week) are expected.

• The hybrid arrangement offers some flexibility in how the work week is structured, balancing onsite collaboration days with remote work.

• Given the critical nature of the role, occasional extended hours or on-call responsibilities may arise, particularly during critical build or deployment phases.

📝 Enhancement Note: The hybrid model is typical for senior technical roles, balancing the need for in-person collaboration and strategic discussions with the benefits of focused remote work. The San Ramon location places the candidate in a prime tech ecosystem.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter from

Inabia will likely conduct an initial screening to assess basic qualifications, experience, and cultural fit.

• Technical Interview(s): Expect multiple rounds of technical interviews focusing on:

  • AI architecture principles, specifically for large language models and multi-agent systems.
  • Deep dives into RAG, vector stores, and embedding models.
  • Identity and access management strategies in distributed systems.
  • Cloud-native architecture and GCP expertise.
  • Python and Go proficiency.
  • Experience with enterprise platform ownership and governance.

• Architectural Design Challenge: A practical exercise may be given, requiring the candidate to design a component of the Governance Control Tower or solve a specific architectural problem. This could be a take-home assignment or a live whiteboard session.

• Stakeholder/Leadership Interview: An interview with platform leadership from Zensar or its end-client to assess strategic thinking, communication skills, and overall fit for the architectural owner role.

• Final Offer: Based on successful completion of all interview stages.

Portfolio Review Tips:

• Highlight Architectural Ownership: Clearly showcase projects where you were the sole or primary architectural owner, detailing your responsibilities, decisions, and the impact of those decisions.

• Quantify AI System Experience: Provide specific examples of production AI systems you designed and operated, including scale, performance metrics, and challenges overcome.

• Demonstrate Governance Expertise: Include examples of how you defined, implemented, and enforced technical standards and governance policies.

• Showcase Gemini/ADK Proficiency: Detail your hands-on experience with these specific technologies, outlining use cases and lessons learned.

• Illustrate RAG and IAM Depth: Prepare to discuss your approach to RAG architectures and complex IAM scenarios with concrete examples.

• Focus on Impact: For each project or case study, articulate the business value, ROI, or efficiency gains achieved through your architectural solutions.

Challenge Preparation:

• System Design: Prepare for system design questions related to scalable, secure, and governed AI platforms. Think about components like agent registries, orchestration layers, retrieval systems, and access control.

• Problem-Solving: Be ready to articulate your thought process for diagnosing and resolving complex technical issues in a production AI environment.

• Scenario-Based Questions: Anticipate questions about how you would handle specific governance challenges, scale an agent fleet, or optimize costs.

• Communication: Practice explaining complex technical concepts clearly and concisely to both technical and non-technical audiences.

📝 Enhancement Note: The emphasis on "architectural owner" and "setting standards" means candidates should prepare to discuss their leadership style, decision-making process, and how they influence technical direction and adoption within an organization. The portfolio should be curated to specifically highlight these aspects.

🛠 Tools & Technology Stack

Primary Tools:

• AI Platforms: Google Gemini Enterprise, Google ADK (Agent Development Kit).

• Programming Languages: Python (required),

Go (preferred).

• Cloud Platform: Google Cloud Platform (GCP) is strongly preferred, with services like:

  • Vertex AI (for ML model deployment and management)
  • Cloud Run (for containerized applications)
  • Google Kubernetes Engine (GKE) (for container orchestration)
  • Identity and Access Management (IAM)

• Containerization: Docker, Kubernetes (GKE).

• Networking: GCP networking concepts.

Analytics & Reporting:

• Observability Tools: Tools for monitoring AI system performance, logging, and tracing (specifics to be determined, but likely GCP-native tools like Cloud Monitoring, Cloud Logging, or third-party solutions).

• FinOps Tools: Tools and techniques for tracking and managing cloud spend, inference costs, and token usage.

CRM & Automation:

• Agent Registry & Gateway: Custom-built or platform-specific components for managing AI agents and their access points.

• Orchestration Tools: Potentially internal or third-party tools for managing multi-agent workflows (e.g., MCP experience).

• Integration Tools: Tools for integrating various components of the AI platform and connecting to external systems.

📝 Enhancement Note: Proficiency in GCP and its AI/ML services (Vertex AI) is a significant advantage. The candidate must demonstrate a deep understanding of how these services integrate to form a robust and scalable AI platform. Experience with MCP (building servers) implies a need for backend development and infrastructure knowledge beyond just consuming services.

👥 Team Culture & Values

Operations Values:

• Technical Excellence: A commitment to designing and building high-quality, reliable, and scalable AI systems.

• Governance & Compliance: Prioritizing security, ethical AI practices, and adherence to defined standards and regulations.

• Data-Driven Decision Making: Utilizing metrics and data to inform architectural choices, operational improvements, and cost management.

• Collaboration & Communication: Fostering an environment of open communication and teamwork within the delivery pod and with stakeholders.

• Innovation & Continuous Improvement: Embracing new technologies and methodologies to enhance the AI platform's capabilities and efficiency.

• Ownership & Accountability: Taking full responsibility for the architecture, implementation, and operational success of the Governance Control Tower.

Collaboration Style:

• Cross-functional Integration: Working closely with development teams, operations, security, and business stakeholders to ensure alignment and successful delivery.

• Proactive Communication: Regularly updating platform leadership and team members on progress, risks, and architectural decisions.

• Mentorship & Knowledge Sharing: Guiding and supporting the delivery pod, and sharing expertise across the organization.

• Constructive Feedback: Participating in design reviews and code reviews, providing and receiving feedback to improve solutions.

📝 Enhancement Note: The culture is likely one of high performance, technical rigor, and a strong emphasis on delivering secure and scalable AI solutions. As an architectural owner, the candidate is expected to embody these values and drive them within their team.

⚡ Challenges & Growth Opportunities

Challenges:

• Complexity of AI Governance: Establishing and enforcing effective governance across a growing fleet of diverse AI agents can be complex.

• Rapidly Evolving AI Landscape: Keeping pace with the fast-changing advancements in AI technology and adapting the architecture accordingly.

• Balancing Innovation with Control: Finding the right balance between enabling rapid AI development and maintaining strict governance and security standards.

• Cost Management at Scale: Optimizing inference and token spend for a large-scale AI deployment while ensuring performance.

• Cross-functional Alignment: Ensuring all stakeholders (engineering, product, business) are aligned on architectural decisions and governance policies.

• Hands-on Leadership: Remaining technically hands-on while also fulfilling architectural ownership and leadership responsibilities.

Learning & Development Opportunities:

• Deep Dive into Gemini Enterprise: Gaining unparalleled hands-on experience with Google's enterprise AI offerings.

• Advanced AI Architecture: Developing expertise in complex multi-agent systems, advanced RAG techniques, and AI orchestration.

• Enterprise AI Governance: Becoming a recognized expert in designing and implementing governance frameworks for AI at scale.

• Cloud Architecture Leadership: Enhancing skills in GCP-specific AI/ML services and cloud-native design patterns.

• FinOps for AI: Developing specialized knowledge in managing the economics of large-scale AI deployments.

• Mentorship: Potentially mentoring junior architects or engineers within the delivery pod.

📝 Enhancement Note: The challenges presented are inherent to cutting-edge AI platform development and governance. Successfully navigating these will significantly enhance the candidate's expertise and marketability in the AI/ML space.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you were the sole architectural owner of an enterprise platform. What were your key responsibilities, and how did you ensure your standards were adopted and maintained?"

  • Preparation: Focus on your decision-making process, influence strategies, and how you managed technical debt and evolution. Use the STAR method.

• "How would you design the top-level agent layer for a platform supporting hundreds of diverse AI agents, considering security, scalability, and reusability?"

  • Preparation: Think about common entry points, orchestration patterns, context management, and security layers. Sketch out a high-level architecture.

• "What are the critical components of an effective AI governance framework, and how would you implement them for Google Gemini Enterprise?"

  • Preparation: Cover aspects like data privacy, bias detection, model validation, access control, and cost monitoring. Relate these to specific Gemini features or ADK capabilities.

• "Describe your experience with RAG architectures. What are the key considerations for chunking, embedding, and retrieval strategies in a production environment?"

  • Preparation: Be ready to discuss different embedding models, vector store types, chunking strategies (fixed size, semantic), and hybrid search methods.

• "How would you ensure identity and entitlement enforcement is robust across a multi-agent system, particularly when dealing with sensitive data?"

  • Preparation: Discuss OAuth2, SAML, RBAC, token propagation, and mapping document-level ACLs to retrieval layer permissions.

Company & Culture Questions:

• "What do you know about Inabia and Zensar, and how does your experience align with the needs of this AI Architect role?"

  • Preparation: Research both companies. Understand Inabia's consulting model and Zensar's position in the tech services market. Highlight your relevant enterprise AI and governance experience.

• "How do you approach leading a cross-functional technical team, especially when implementing a new, complex platform like this Governance Control Tower?"

  • Preparation: Discuss your leadership style, how you set direction, manage performance, and foster collaboration.

• "What are your thoughts on the balance between enabling rapid AI innovation and implementing strict governance controls?"

  • Preparation: Articulate a balanced perspective, emphasizing how governance can actually enable faster, safer innovation.

Portfolio Presentation Strategy:

• Focus on Architecture & Ownership: Select case studies that clearly demonstrate your role as an architectural owner and your ability to define and enforce standards.

• Quantify Impact: For each project, highlight metrics related to reliability, scalability, cost savings, or risk reduction achieved through your architectural decisions.

• Showcase Gemini/ADK Experience: If possible, include examples of projects where you used these specific technologies or similar enterprise AI frameworks.

• Explain the "Why": Clearly articulate the business problem, your architectural solution, and the resulting business value.

• Be Prepared for Deep Dives: Anticipate detailed questions about your design choices, trade-offs, and implementation challenges.

📝 Enhancement Note: The interview process is designed to assess deep technical expertise, architectural leadership, and the ability to manage complex, high-stakes projects in a cutting-edge field. Candidates should prepare to defend their architectural decisions and demonstrate a strategic mindset.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided link on the Inabia job portal.

• Portfolio Customization: Tailor your resume and any supplementary materials to highlight your experience as an "architectural owner," your work with enterprise AI platforms (especially Gemini Enterprise), your expertise in RAG and IAM, and your experience in setting and enforcing technical standards.

• Resume Optimization: Ensure your resume clearly details your 10+ years of software engineering and architecture experience, with a specific focus on the last 3+ years in applied AI systems. Use keywords from the job description such as "Gemini Enterprise," "AI Governance," "RAG," "Python," "GCP," "Multi-agent Systems," and "Identity & Access Management." Quantify your achievements wherever possible.

• Interview Preparation: Thoroughly review potential interview questions, practice articulating your experience using the STAR method, and prepare specific examples for architectural design challenges and governance scenarios. Rehearse presenting your portfolio highlights.

• Company Research: Familiarize yourself with Inabia Software & Consulting Inc. and Zensar Technologies. Understand their service offerings and how this role fits into their client delivery model. Research best practices in AI governance and Gemini Enterprise architecture to demonstrate your engagement and understanding.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with Inabia Software & Consulting Inc. or Zensar Technologies before making application decisions. The "NO C2C" designation is critical and should be respected by applicants.