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📍 Job Overview

Job Title: Gemini AI Architect - Governance Control Tower

Company: Inabia Software & Consulting Inc.

Location: San Ramon, CA

Job Type: CONTRACTOR

Category: Revenue Operations / AI Architecture

Date Posted: 2026-08-20

Experience Level: 10+ Years

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

🚀 Role Summary

• Architectural ownership and end-to-end design of an enterprise AI Governance Control Tower, ensuring comprehensive oversight and control for AI agent deployment and operation.

• Define and enforce critical architecture standards, quality gates, and implementation checklists for all AI agents before they go live, maintaining platform reliability and security.

• Lead and manage a cross-functional delivery pod, providing technical direction, reviewing work, and ensuring successful implementation and ongoing operation of the AI platform.

• Serve as the primary technical liaison between platform leadership and the delivery team, translating strategic direction into actionable architecture and robust implementation plans.

• Drive operational excellence across key workstreams including 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 of AI systems, specifically within an enterprise context using Google Gemini Enterprise. The emphasis on "architectural owner" and "platform leadership deals with directly" signifies a senior, strategic position with significant accountability. The "Governance Control Tower" concept suggests a central hub for managing and monitoring AI agent behavior, compliance, and performance. This is a GTM-adjacent role, as robust AI governance directly impacts the safe and effective deployment of AI-powered solutions, which in turn affects go-to-market strategies for AI-driven products and services.

📈 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 the cross-functional delivery pod by setting technical direction, conducting rigorous work reviews, and maintaining ultimate accountability for the successful deployment of AI solutions.

• Effectively translate high-level strategic direction from platform leadership into detailed architectural specifications and defendable implementation plans.

• Uphold and enforce a high quality bar for the AI platform through the diligent use of evaluation datasets, threshold gates, and regression testing to ensure sustained reliability as agent volume scales.

• Assume end-to-end architectural responsibility for the following critical workstreams: Governance & Standards, Registry & Gateway Operations, Connector & Retrieval Engineering, Identity & Entitlement Enforcement, and Observability & Cost Control.

• Develop and maintain comprehensive checklists and standards that every AI agent must clear before deployment, ensuring adherence to governance and compliance requirements.

• Manage the review board relationship, presenting architectural designs and implementation progress to key stakeholders.

• Oversee the operational transition from a build phase (standing up the Control Tower) to a run phase, ensuring a right-sized support pod is in place while retaining architectural ownership.

📝 Enhancement Note: The responsibilities highlight a blend of strategic architectural design, hands-on technical leadership, and operational management. The emphasis on "quality bar," "evaluation datasets," and "regression testing" points to a critical need for robust processes and a quality-first mindset, which are core to effective operations. The specific mention of "Registry & Gateway Operations" and "Identity & Entitlement Enforcement" are direct operational functions crucial for managing access and deployment of AI agents.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience. A Master's degree or Ph.D. in a relevant technical discipline is a strong plus.

Experience:

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

• Minimum of 3 years of direct experience designing, implementing, and operating applied AI systems in production environments.

• Demonstrated track record as the sole architectural owner of an enterprise-scale platform, successfully setting and enforcing standards across multiple teams. Required Skills:

• Hands-on expertise with Google Gemini Enterprise and Google ADK, or a directly comparable enterprise-grade agent platform, including deep understanding of its runtime, registration, identity, and observability features.

• Proven experience with multi-agent systems in a production setting, encompassing orchestration, routing, tool utilization, memory management, and human-in-the-loop workflows, with demonstrable operational ownership.

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

• In-depth understanding of 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.

• Proficiency in Python is essential; a strong working knowledge of Go or another equivalent second language is highly desirable.

• Experience with Managed Control Plane (MCP) environments, specifically in building servers rather than just consuming them.

• Solid grasp of cloud-native principles and systems fundamentals, with a strong preference for Google Cloud Platform (GCP), including experience with Cloud Run, GKE, Vertex AI, networking configurations, and IAM.

• Demonstrated awareness of cost management at scale, including strategies for managing token and inference spend across a growing estate of AI agents. Preferred Skills:

• Experience with advanced orchestration frameworks and context engineering techniques.

• Familiarity with FinOps principles and practices applied to AI/ML workloads.

• Knowledge of specific enterprise AI governance frameworks and compliance requirements.

• Experience in building and operating agent registries and gateways.

📝 Enhancement Note: The requirement for 10+ years of experience and 3+ years in applied AI systems clearly positions this as a senior-level architectural role. The emphasis on hands-on experience with specific Google AI technologies (Gemini Enterprise, ADK, GCP services) and core AI/ML concepts (RAG, multi-agent systems, vector stores) is critical. The inclusion of identity and access management, networking, and cost control highlights the operational and security-focused aspects of this role, which are paramount for enterprise-grade AI deployments.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Case Studies in AI Governance: Showcase a minimum of 2-3 detailed case studies demonstrating your experience in designing and implementing AI governance frameworks, including policy definition, compliance monitoring, and risk mitigation strategies.

• Platform Architecture Designs: Include architectural diagrams and documentation for enterprise platforms you have owned end-to-end, particularly those involving AI/ML components, highlighting scalability, security, and operational efficiency.

• System Implementation Examples: Provide examples of complex systems you have built or significantly contributed to, specifically detailing the implementation of agent orchestration, retrieval mechanisms, or identity management solutions.

• ROI & Efficiency Demonstrations: For each relevant project, clearly articulate the business impact, including metrics related to cost savings, performance improvements, enhanced reliability, or risk reduction achieved through your architectural decisions.

Process Documentation:

• Workflow Design & Optimization: Present examples of documented workflows for AI agent lifecycle management, from development and testing to deployment and ongoing monitoring, with a focus on optimization and automation.

• Implementation & Automation Methods: Detail the methodologies and tools used for implementing and automating AI platform components, including CI/CD pipelines for AI models and infrastructure as code practices.

• Measurement & Performance Analysis: Demonstrate your approach to defining Key Performance Indicators (KPIs) for AI systems, including reliability, performance, cost, and security metrics, and how you've used data to drive continuous improvement.

📝 Enhancement Note: For a senior architectural role like this, a portfolio is crucial. It should not only showcase technical design skills but also the ability to manage complex systems, enforce standards, and demonstrate tangible business value. The emphasis on "process documentation" underscores the need for systematic approaches to AI governance and operations.

💵 Compensation & Benefits

Salary Range:

• Estimated Range: $55 - $75 per hour (W-2 for Inabia)

• Explanation: This estimate is based on the provided rate of $55/hr. for Inabia W-2, adjusted upwards to reflect the senior architectural nature of the role, the high demand for specialized AI/ML architecture skills, and the location in the San Francisco Bay Area. The upper end of the range accounts for candidates with exceptionally strong qualifications and direct experience with the specified technologies. Salary data was cross-referenced with industry benchmarks for AI Architects and Senior Software Engineers in the San Francisco Bay Area, considering the contract employment type.

Benefits:

• Health, Dental, and Vision Insurance: Comprehensive coverage options for medical, dental, and vision.

• 401(k) Retirement Plan: Opportunities for retirement savings with potential company match.

• Paid Time Off (PTO): Accrued paid time off for vacation, sick leave, and personal days.

• Professional Development: Access to training, certifications, and conferences to enhance AI and architectural skills.

• Life and Disability Insurance: Coverage to provide financial security for employees and their families.

Working Hours:

• Standard 40-hour work week.

• Hybrid Work Arrangement: Minimum of 3 days per week required onsite in San Ramon, CA, offering a blend of remote flexibility and in-office collaboration. This structure is designed to facilitate team synergy and direct engagement with platform leadership.

📝 Enhancement Note: The provided rate of $55/hr. (Inabia W-2) is a key data point. The estimated range accounts for the senior nature and specialized skills required. Benefits for contract roles can vary; the listed benefits are standard for W-2 employees of staffing agencies like Inabia. The hybrid requirement with specific onsite days is a critical detail for candidates.

🎯 Team & Company Context

🏢 Company Culture

Industry: Software & Consulting Services, with a strong focus on emerging technologies like Artificial Intelligence and Generative AI.

Company Size: Inabia Software & Consulting Inc. is a mid-sized firm, likely employing between 50-250 employees based on typical consulting firm structures. This size often allows for a balance of established processes and agile adaptability.

Founded: Inabia was founded in 2011, indicating over a decade of experience in the technology consulting space, providing a foundation of expertise and market presence.

Team Structure:

• AI Architecture & Governance Pod: This role leads a dedicated, cross-functional delivery pod. The pod likely comprises engineers, AI specialists, and potentially operations analysts focused on the Governance Control Tower.

• Reporting Structure: The Architect will report directly to platform leadership, acting as the primary technical point of contact for the Control Tower initiative. Day-to-day management of the pod will be part of the role's leadership responsibilities.

• Cross-functional Collaboration: Expect extensive collaboration with various internal teams (e.g., engineering, product, security, compliance, finance) and potentially with the end client's (Zensar) teams, especially during the build and operational transition phases.

Methodology:

• Data-Driven Design: Emphasis on using data to inform architectural decisions, set quality standards, and measure performance through evaluation datasets and observability metrics.

• Agile & Iterative Development: The "delivery pod" model suggests agile methodologies, with iterative implementation and continuous feedback loops during both build and run phases.

• DevOps/MLOps Integration: The role's focus on CI/CD, infrastructure, and operational ownership implies a strong integration of development and operations principles for AI systems.

Company Website: https://www.inabia.com/

📝 Enhancement Note: Inabia's background in software and consulting, coupled with a focus on AI, suggests a culture that values technical expertise, innovation, and client-centric solutions. The mid-size nature often means a more direct impact for individuals and less bureaucracy than a very large organization. The "Governance Control Tower" project for Zensar indicates a significant engagement with a larger enterprise client.

📈 Career & Growth Analysis

Operations Career Level: This is a Principal-level or Lead Architect position, signifying a highly experienced professional responsible for strategic technical direction and architectural ownership of a critical platform component. The role demands deep technical expertise combined with leadership capabilities.

Reporting Structure: The AI Architect will report directly to senior platform leadership, likely a VP or Director of Engineering/AI. They will lead a specialized delivery pod, acting as the central technical authority for the Governance Control Tower.

Operations Impact: This role has a profound impact on the operational integrity, security, compliance, and cost-effectiveness of the client's AI initiatives. By establishing robust governance and control mechanisms, the architect directly influences the scalability, reliability, and responsible deployment of AI solutions, which are foundational to the client's overall business strategy and go-to-market capabilities for AI-driven products.

Growth Opportunities:

• Deep Specialization: Opportunity to become a recognized expert in enterprise AI governance, RAG architectures, and multi-agent systems, leading to further specialized roles or consultancy.

• Leadership Expansion: Potential to expand leadership responsibilities to encompass broader AI platform architecture or to manage multiple delivery pods.

• Client Relationship Management: Develop stronger relationships with enterprise clients like Zensar, potentially leading to long-term engagements or opportunities within client organizations.

• Industry Influence: Contributing to the development of best practices and standards in AI governance, potentially leading to speaking engagements or publications.

📝 Enhancement Note: This is not a traditional "operations" role focused on CRM or sales ops, but rather on the operationalization and governance of cutting-edge AI technology. The growth path is within AI architecture and leadership, with a direct link to business success through enabling safe and scalable AI deployment.

🌐 Work Environment

Office Type: The role requires a hybrid work arrangement, meaning a combination of remote work and in-office presence. This suggests a modern work environment that balances flexibility with the need for in-person collaboration.

Office Location(s): San Ramon, CA. This location is part of the greater San Francisco Bay Area, known for its tech ecosystem and access to talent. The office will likely be equipped with modern amenities to support collaborative work.

Workspace Context:

• Collaborative Spaces: The office environment will likely feature meeting rooms, project spaces, and common areas designed to foster interaction and brainstorming among the delivery pod and other Inabia colleagues.

• Technology & Tools: Access to necessary hardware, software, and cloud resources (e.g., GCP) will be provided. The role involves hands-on work with advanced AI platforms and development tools.

• Team Interaction: Regular opportunities for direct interaction with the delivery pod members, project managers, and Inabia leadership, as well as potential interactions with client stakeholders from Zensar.

Work Schedule:

• The standard 40-hour work week applies, with the flexibility of remote work days.

• The hybrid model (minimum 3 days onsite) requires careful planning to ensure effective collaboration, especially for critical architectural reviews and team syncs. This schedule supports deep work on complex architectural problems while enabling face-to-face engagement.

📝 Enhancement Note: The hybrid nature of the role is a key aspect of the work environment, balancing flexibility with the need for physical presence in San Ramon. This setup is common in the tech consulting industry, aiming to maximize both individual productivity and team cohesion.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A preliminary call with an Inabia recruiter to assess basic qualifications, experience, and alignment with the role's core requirements.

• Technical Interview 1 (Architectural Deep Dive): In-depth discussion focusing on your experience with AI architecture, RAG, multi-agent systems, and enterprise platform ownership. Expect scenario-based questions related to designing and governing AI systems. You will be asked to discuss your approach to setting standards and quality gates.

• Portfolio Review Session: A dedicated session to walk through your selected portfolio pieces. Be prepared to articulate your design choices, the challenges faced, the solutions implemented, and the measurable impact (ROI, efficiency gains). Focus on your architectural ownership and governance experience.

• Technical Interview 2 (Hands-on/Problem Solving): This may involve a coding exercise (likely Python) or a system design challenge related to the Governance Control Tower's components (e.g., designing an agent registry, an identity enforcement mechanism, or a RAG pipeline).

• Client Stakeholder Interview: A final interview with key stakeholders from Zensar (the end client) to assess cultural fit, communication skills, and strategic alignment. They will want to understand your ability to lead and translate technical direction.

Portfolio Review Tips:

• Curate Strategically: Select 2-3 high-impact projects that best demonstrate your experience as an architectural owner of enterprise platforms, specifically highlighting AI governance, RAG, multi-agent systems, and IAM.

• Structure Your Narratives: For each project, clearly outline the problem, your role and responsibilities (emphasizing ownership), the architectural solution, the technical challenges and how you overcame them, and the quantifiable outcomes (e.g., improved reliability by X%, reduced costs by Y%, enhanced security posture).

• Focus on Governance & Control: Explicitly detail how you established standards, implemented quality gates, managed risk, and ensured compliance within your past projects.

• Demonstrate Technical Depth: Be prepared to discuss the specifics of your technical choices, including vector stores, embedding models, orchestration strategies, and IAM protocols.

• Practice Your Presentation: Rehearse walking through your portfolio concisely and engagingly. Be ready to answer detailed questions about your design decisions and their rationale.

Challenge Preparation:

• AI Governance Scenarios: Prepare for questions asking how you would address specific governance challenges, such as controlling AI agent behavior, ensuring data privacy, or managing AI model drift.

• System Design for Control Tower: Practice designing components of the Governance Control Tower. Consider how to build a scalable agent registry, a robust identity and entitlement enforcement system, or an effective observability framework for AI agents.

• Python Proficiency: Brush up on Python, particularly for tasks related to API interactions, data processing, and potentially building backend services.

• Cloud-Native Concepts: Be ready to discuss GCP services relevant to building and managing distributed systems and AI platforms.

📝 Enhancement Note: The interview process is designed to rigorously assess both deep technical expertise and the ability to lead and manage complex projects within an enterprise client context. The portfolio review is central, requiring candidates to showcase tangible evidence of their architectural ownership and governance capabilities.

🛠 Tools & Technology Stack

Primary Tools:

• Google Gemini Enterprise & Google ADK: Core platform for building and deploying generative AI agents. Deep, hands-on experience is mandatory.

• Python: Primary programming language for development, scripting, and automation.

• Go (or equivalent): Desirable second language for backend services and systems development.

• MCP (Managed Control Plane): Experience building servers within this environment is a significant plus.

Analytics & Reporting:

• Observability Tools: Experience with tools for monitoring AI agent performance, health, and resource utilization (e.g., Google Cloud Operations Suite, Prometheus, Grafana, Datadog).

• FinOps Tools/Practices: Understanding and ability to implement cost management and optimization strategies for AI workloads.

• Data Analysis Tools: Proficiency in analyzing performance metrics, logs, and cost data to identify trends and drive improvements.

CRM & Automation:

• While not a traditional CRM/Sales Ops role, understanding of how AI systems integrate with business processes is beneficial.

• CI/CD Tools: Experience with tools for continuous integration and continuous deployment of AI models and infrastructure (e.g., Jenkins, GitLab CI, GitHub Actions, Vertex AI Pipelines).

• Infrastructure as Code (IaC): Familiarity with tools like Terraform or CloudFormation for managing cloud infrastructure.

Cloud Platform:

• Google Cloud Platform (GCP): Essential, with specific experience in:

  • Cloud Run: Serverless container execution.

  • GKE (Google Kubernetes Engine): Managed Kubernetes service.

  • Vertex AI: End-to-end ML platform.

  • Networking: VPCs, firewalls, load balancing.

  • IAM (Identity and Access Management): For managing permissions and access. Data & AI Specific:

• Vector Stores: Experience with databases like Pinecone, Weaviate, ChromaDB, or managed cloud offerings.

• Embedding Models: Understanding of various embedding techniques and models.

• RAG Frameworks: Familiarity with libraries or frameworks that facilitate RAG implementations.

📝 Enhancement Note: The technology stack is heavily skewed towards Google Cloud Platform and cutting-edge AI/ML technologies. Proficiency in Python and a strong understanding of cloud-native architecture, containerization, and AI-specific tools like vector stores and RAG frameworks are critical. The inclusion of "MCP" and "building servers" suggests a need for deep system-level understanding beyond typical application development.

👥 Team Culture & Values

Operations Values:

• Technical Excellence & Ownership: A strong emphasis on deep technical understanding, architectural integrity, and taking full ownership of the platform's design and operational success.

• Reliability & Quality: Commitment to building robust, reliable systems through rigorous testing, quality gates, and continuous monitoring. Process adherence is key to maintaining this.

• Innovation & Adaptability: Embracing new AI technologies and methodologies, with a willingness to adapt and evolve the platform as the AI landscape changes.

• Collaboration & Communication: Fostering a collaborative environment where clear communication, constructive feedback, and cross-functional teamwork are paramount for success.

• Efficiency & Cost Consciousness: A focus on optimizing resource utilization and managing costs effectively, particularly in the context of large-scale AI deployments.

Collaboration Style:

• Pod-Based Teamwork: The "delivery pod" model promotes a highly integrated and collaborative working style, where team members work closely together on shared goals.

• Cross-Functional Integration: This role requires significant collaboration with various departments, necessitating strong communication skills to bridge technical and business perspectives.

• Feedback-Driven Improvement: An environment where continuous feedback is encouraged to refine processes, architectures, and operational practices.

• Knowledge Sharing: A culture that supports sharing best practices, lessons learned, and technical insights across the team and with stakeholders.

📝 Enhancement Note: The culture likely values proactive problem-solving, deep technical expertise, and a strong sense of responsibility. The emphasis on "ownership" and "quality" suggests a mature operational mindset applied to AI development.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapidly Evolving AI Landscape: Keeping pace with the continuous advancements in AI technology, particularly in generative AI and multi-agent systems, and adapting the architecture accordingly.

• Balancing Innovation with Governance: Finding the right balance between enabling rapid AI development and innovation while enforcing strict governance, security, and compliance standards.

• Complex System Integration: Integrating diverse AI components, data sources, and identity management systems into a cohesive and functional Governance Control Tower.

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

• Stakeholder Alignment: Managing expectations and ensuring buy-in from various stakeholders, including platform leadership and the end client (Zensar), who may have differing priorities.

Learning & Development Opportunities:

• Cutting-Edge AI Exposure: Direct work with Google Gemini Enterprise, ADK, and advanced RAG techniques provides unparalleled learning in state-of-the-art AI.

• Enterprise AI Governance Expertise: Developing deep specialization in the critical field of AI governance and control, a high-demand skill.

• Cloud Architecture Mastery: Deepening expertise in GCP services and cloud-native architectures for AI/ML workloads.

• Leadership and Mentorship: Opportunities to mentor junior engineers and lead technical initiatives, enhancing leadership capabilities.

• Industry Conferences & Training: Potential for participation in leading AI and cloud technology conferences and specialized training programs.

📝 Enhancement Note: The challenges are inherent to working with bleeding-edge AI technology at an enterprise scale. The growth opportunities are significant, offering a path to becoming a leader in a highly sought-after field.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you were the sole architectural owner of an enterprise platform. What were the biggest challenges, and how did you ensure your standards were adopted and maintained?" (Focus on your process for setting standards, enforcing them, and managing resistance.)

• "How would you design a RAG system for a large enterprise with diverse data sources, ensuring both accuracy and robust access controls?" (Prepare to discuss data ingestion, chunking, embedding strategies, vector store selection, and the integration of identity/entitlement enforcement.)

• "Imagine a scenario where a new AI agent deployed on the platform is exhibiting unexpected behavior or incurring excessive costs. What steps would you take to diagnose and rectify the situation using your Governance Control Tower?" (Emphasize your observability, cost control, and governance processes.) Company & Culture Questions:

• "What interests you most about Inabia Software & Consulting Inc. and this specific role with Zensar?" (Research Inabia's mission and Zensar's industry to tailor your response.)

• "How do you approach collaboration with platform leadership and technical teams? Can you provide an example of translating complex technical concepts for non-technical stakeholders?" (Highlight your communication and translation skills.)

• "Describe your experience with managing technical debt and ensuring the long-term maintainability and reliability of a platform." (Showcase your understanding of operational excellence and proactive management.) Portfolio Presentation Strategy:

• Tell a Story: Frame your portfolio projects as narratives of problem-solving, innovation, and impact.

• Quantify Everything: For each project, present clear metrics on performance improvements, cost savings, efficiency gains, risk reduction, or scalability achieved. Use numbers wherever possible.

• Highlight Governance & Ownership: Explicitly call out your role as the architectural owner and how you implemented governance, standards, and control mechanisms.

• Visual Aids: Use clear, concise diagrams (architecture, workflows) to illustrate your technical solutions.

• Anticipate Questions: Be prepared for deep technical dives into your design choices, trade-offs considered, and alternative solutions you evaluated.

📝 Enhancement Note: Interview preparation should focus on demonstrating not just technical prowess but also leadership, strategic thinking, and the ability to manage complex, high-stakes projects. The portfolio is your primary tool for showcasing this.

📌 Application Steps

To apply for this operations position:

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

• Tailor Your Resume: Emphasize your 10+ years of software engineering experience, specifically highlighting your 3+ years in applied AI systems and any experience as an "architectural owner" of enterprise platforms. Integrate keywords like "Google Gemini Enterprise," "RAG," "AI Governance," "multi-agent systems," "Python," "GCP," and "IAM."

• Prepare Your Portfolio: Curate 2-3 of your strongest projects that best showcase your experience in designing, implementing, and governing complex AI platforms. Ensure each case study clearly articulates the problem, your ownership, the solution, and quantifiable results.

• Practice Your Narrative: Rehearse explaining your portfolio projects and your approach to AI governance and architecture. Be ready to discuss technical details and strategic decisions.

• Research: Familiarize yourself with Inabia Software & Consulting Inc., Zensar, and the broader landscape of AI governance and generative AI trends. Understand the responsibilities of a "Governance Control Tower."

⚠️ 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 before making application decisions.