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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/ML Architecture & Governance

Date Posted: August 20, 2026

Experience Level: 10+ Years

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

🚀 Role Summary

• This role is the primary architectural owner for a critical AI Governance Control Tower, responsible for its end-to-end design, implementation, and operational oversight within a complex enterprise environment.

• It requires defining stringent architecture standards, operational checklists, and leading a cross-functional delivery pod to ensure the reliable and secure deployment of AI agents.

• The position involves hands-on engagement across multiple workstreams including Governance & Standards, Registry & Gateway Operations, Connector & Retrieval Engineering, Identity & Entitlement Enforcement, and Observability & Cost Control.

• Success in this role hinges on translating high-level platform direction into actionable architecture, maintaining a high quality bar for AI systems, and managing direct relationships with platform leadership.

📝 Enhancement Note: This role is highly specialized, focusing on the architectural leadership of an AI Governance Control Tower. It bridges advanced AI/ML engineering, enterprise architecture, and operational governance. The emphasis on "architectural owner" and "platform leadership deals with directly" indicates a senior, high-impact position with significant autonomy and responsibility. The "contractor" designation suggests a project-based engagement, likely long-term given the "Long Term" duration mentioned.

📈 Primary Responsibilities

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

• Lead a dedicated cross-functional delivery pod, providing technical direction, conducting thorough work reviews, and maintaining ultimate accountability for the successful shipment of AI platform components.

• Act as the key liaison between platform leadership and the delivery team, effectively translating strategic directives into robust architectural blueprints and developing defensible implementation plans.

• Establish and enforce a high quality bar for the AI platform, defining rigorous evaluation datasets, threshold gates, and regression testing protocols to ensure sustained reliability as the agent ecosystem scales.

• Assume architectural ownership and drive initiatives across the five core workstreams: Governance & Standards, Registry & Gateway Operations, Connector & Retrieval Engineering, Identity & Entitlement Enforcement, and Observability & Cost Control.

• Develop and implement comprehensive identity and access management strategies, including OAuth2, SAML, RBAC, and secure credential propagation, ensuring granular control and compliance.

• Oversee the implementation and operation of retrieval architecture, including vector stores, embedding models, chunking strategies, and hybrid search capabilities, to optimize data access and relevance.

• Manage and optimize the cost of AI operations, focusing on token and inference spend management across a growing estate of AI agents, aligning with FinOps principles.

📝 Enhancement Note: The responsibilities clearly delineate a senior architect role with direct leadership and ownership. The emphasis on "design the reusable top-level agent layer" and "translate between platform leadership and the delivery team" highlights strategic and communication duties alongside technical architecture. The specific mention of workstreams like "Registry & Gateway Operations" and "Identity & Entitlement Enforcement" indicates a focus on operationalizing AI systems securely and efficiently.

🎓 Skills & Qualifications

Education:

• While no specific degree is listed, candidates are expected to possess a strong theoretical and practical foundation in computer science, software engineering, or a related field, demonstrated through extensive experience. Experience:

• A minimum of 10 years in software engineering and architecture is required.

• A minimum of 3 years of hands-on experience designing and operating applied AI systems in a production environment is mandatory.

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

• Experience leading cross-functional delivery pods or engineering teams is essential.

• Deep operational ownership experience with multi-agent systems, including orchestration, routing, tool use, memory management, and human-in-the-loop workflows. Required Skills:

• Applied AI Systems: 3+ years designing and running applied AI systems in production.

• Enterprise AI Platforms: Hands-on experience with Google Gemini Enterprise and Google ADK, or comparable enterprise agent platforms. This includes deep understanding of runtime, registration, identity management, and observability features.

• Multi-Agent Systems: Extensive experience in production environments with multi-agent systems, covering orchestration, routing, tool use, memory, and human-in-the-loop (HITL) mechanisms.

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

• Identity & Access Management: Deep expertise in identity and access controls, including OAuth2, SAML, Role-Based Access Control (RBAC), token exchange, service-account vs. end-user credential propagation, and mapping document-level ACLs to retrieval layers.

• Programming Languages: Proficient in Python. Comfortable with Go or an equivalent second language.

• Platform Operations: Experience with MCP (likely referring to a specific AI platform or framework, e.g., Google's Multimodal Conversation Platform or a similar concept), with experience building servers, not just consuming them.

• Cloud-Native Fundamentals: Solid understanding of cloud-native principles and systems fundamentals, with a strong preference for Google Cloud Platform (GCP).

• Cost Management: Awareness of cost implications at scale, specifically token and inference spend management for a large agent estate.

Preferred Skills:

• GCP Services: Specific experience with GCP services such as Cloud Run, Google Kubernetes Engine (GKE), Vertex AI, networking configurations, and Identity and Access Management (IAM).

• AI Governance Frameworks: Familiarity with broader AI governance frameworks and best practices beyond the immediate technical implementation.

• FinOps: Experience or understanding of Financial Operations (FinOps) principles applied to cloud and AI resource management.

📝 Enhancement Note: The requirements emphasize deep, hands-on experience with specific enterprise AI technologies and complex architectural challenges. The "10+ years" and "3+ years in applied AI" clearly position this as a senior-level role. The detailed breakdown of required skills, particularly in RAG, identity management, and specific cloud platforms, is crucial for candidates to assess their fit. The mention of "MCP" suggests a need for familiarity with a specific, potentially proprietary, AI development framework.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Architectural Blueprints: Showcase examples of designing complex, scalable, and reusable architectural layers for enterprise platforms, particularly those involving AI or distributed systems.

• AI System Implementation Case Studies: Provide detailed case studies demonstrating the design, implementation, and operationalization of applied AI systems in production, highlighting challenges overcome and solutions implemented.

• Governance & Standards Documentation: Include examples of architecture standards, operational checklists, or quality assurance frameworks developed and successfully implemented for AI systems or enterprise platforms.

• Cost Optimization Strategies: Present examples of how you have managed and optimized token and inference spend for AI services at scale, demonstrating an understanding of FinOps principles.

• Identity & Access Management Designs: Illustrate designs for robust identity and access management systems, including RBAC, OAuth2, or SAML integrations within an AI or cloud context.

Process Documentation:

• Workflow Design & Optimization: Demonstrate experience in designing and documenting complex workflows for AI agent deployment, operation, and governance, including orchestration and retrieval processes.

• Implementation & Automation: Provide evidence of leading the implementation of AI platforms and features, with a focus on automation strategies for deployment, monitoring, and cost control.

• Measurement & Performance Analysis: Showcase how you have established metrics and reporting for AI system reliability, performance, and cost, including regression testing and threshold gate implementation.

📝 Enhancement Note: For a role of this seniority and specialization, a portfolio is critical. It should not just list technologies but demonstrate the candidate's ability to architect, implement, and govern complex AI systems. Emphasis should be placed on quantifiable achievements, strategic thinking, and hands-on technical depth, particularly in the areas of AI governance, multi-agent systems, and cost management.

💵 Compensation & Benefits

Salary Range:

• Estimated Range: $55 - $75 USD per hour (based on Inabia W-2)

• Methodology: This estimate is derived from the stated rate of $55/hr for an Inabia W-2 contractor, combined with market research for similar senior AI Architect roles in the San Ramon, CA area requiring 10+ years of experience and specialized skills in enterprise AI platforms like Google Gemini Enterprise. The higher end of the range accounts for candidates with exceptional experience, a proven track record in platform ownership, and deep expertise in all required technical domains. The specific rate will depend on the candidate's qualifications and negotiation.

Benefits:

• Contractor Benefits (via Inabia): While specific benefits are not detailed, typical contractor benefits through agencies like Inabia may include:

  • Health, Dental, and Vision Insurance options.
  • Retirement savings plan (e.g., 401k).
  • Paid Time Off (PTO) or Sick Leave accrual, depending on contract terms.
  • Potential for direct deposit and payroll support.

• Project-Specific Considerations: As an end-client engagement with Zensar, the role may offer exposure to enterprise-level projects and advanced AI technologies, providing significant professional development opportunities.

Working Hours:

• Standard: Approximately 40 hours per week.

• Flexibility: While a hybrid model is required with a minimum of 3 days onsite, specific daily hours may offer some flexibility, common in senior technical roles, to accommodate project needs and effective workflow management.

📝 Enhancement Note: The provided rate of $55/hr is for Inabia's W-2 contractors. This implies Inabia is the employer of record, handling payroll, taxes, and potentially offering benefits. The salary range has been estimated to reflect market competitiveness for a senior AI Architect with specialized skills in a high-cost-of-living area like San Ramon, CA. The specific benefits package would need to be confirmed with Inabia.

🎯 Team & Company Context

🏢 Company Culture

Industry: Software & Consulting (Inabia), Technology Services & Consulting (Zensar)

• Inabia Software & Consulting Inc. operates within the IT consulting and staffing sector, providing specialized talent to clients. Zensar, the end client, is a global technology services and digital solutions company, suggesting a corporate environment focused on client delivery, innovation, and digital transformation. This context implies a professional, client-facing culture with an emphasis on technical excellence and project execution. Company Size:

• Inabia: Company size data is not readily available but is typically considered a medium-sized firm in the staffing and consulting industry.

• Zensar: Zensar is a large enterprise, with employee counts often cited in the tens of thousands globally, indicating a vast organizational structure with established processes and a broad reach.

Founded:

• Inabia: Founded in 2015.

• Zensar: Founded in 1991. The long history of Zensar suggests a stable, well-established organization with deep industry experience.

Team Structure:

• Operations Team Aspect 1: The role sits within a "delivery pod," which is a cross-functional team responsible for implementing and operating the Governance Control Tower. This pod likely includes engineers, architects, and potentially operations specialists.

• Operations Team Aspect 2: The AI Architect is the singular "architectural owner" and reports directly to platform leadership, indicating a high-visibility position with a direct reporting line to senior decision-makers.

• Operations Team Aspect 3: Significant cross-functional collaboration is expected with various application teams and stakeholders who will utilize the Governance Control Tower. The role also involves translating between leadership and the delivery team.

Methodology:

• Data Analysis & Insights: The role requires a deep understanding of AI system performance data, cost metrics, and governance compliance to drive insights and improvements.

• Workflow Planning & Optimization: Core to the role is designing and optimizing workflows for AI agent deployment, orchestration, and retrieval, ensuring efficiency and reliability.

• Automation & Efficiency Practices: Implementing and operating the Control Tower involves leveraging automation for deployment, monitoring, and management to achieve operational efficiency and cost control.

Company Website: https://www.inabia.com/ (Inabia), https://www.zensar.com/ (Zensar)

📝 Enhancement Note: The dual company mention (Inabia as the hiring entity, Zensar as the end client) is common in contracting roles. Understanding Zensar's scale and industry is crucial for grasping the project's complexity and the environment in which the Control Tower will operate. The "delivery pod" structure suggests an agile, team-oriented approach to development and operations.

📈 Career & Growth Analysis

Operations Career Level:

• This role represents a senior-level position, specifically an AI Architect with a focus on Governance and Control. It is positioned as the ultimate architectural authority for a critical platform component, requiring extensive experience and strategic foresight. The scope includes end-to-end design, implementation leadership, and ongoing operational ownership, placing it at the top tier of technical individual contributor roles in this domain. Reporting Structure:

• The AI Architect is the sole owner of the Governance Control Tower's architecture and reports directly to "platform leadership." This implies a high degree of visibility and direct interaction with senior management responsible for the overall AI platform strategy. The architect also leads a "cross-functional delivery pod," meaning they will direct the work of a team of engineers and specialists. Operations Impact:

• The impact of this role is substantial, directly influencing the reliability, security, scalability, and cost-effectiveness of the client's AI platform. By establishing and enforcing governance standards, the architect ensures that AI agents are deployed responsibly and ethically, mitigating risks associated with AI systems. This role is pivotal in enabling the organization to leverage AI at scale while maintaining control and compliance. Growth Opportunities:

• Operations Skill Advancement: Deepen expertise in advanced AI architectures, multi-agent systems, RAG, and enterprise AI governance frameworks. Opportunity to gain hands-on experience with cutting-edge technologies like Google Gemini Enterprise at an enterprise scale.

• Leadership Development: Develop leadership skills by managing a cross-functional delivery pod, influencing platform strategy, and directly engaging with senior leadership.

• Specialization: Potential to become a recognized expert in AI governance and control tower architecture, opening doors to further senior architect or principal engineer roles in AI/ML operations and platform engineering.

• Client Exposure: Gain valuable experience working with a large, global technology services company (Zensar), understanding their client delivery models and operational challenges.

📝 Enhancement Note: This role is not a typical "operations" role focused on CRM or sales enablement. It's deeply technical, focusing on the architecture and governance of advanced AI systems. The growth path is within specialized AI/ML architecture and platform engineering leadership.

🌐 Work Environment

Office Type: Hybrid Work Environment

• The role requires a minimum of 3 days per week onsite at the San Ramon, CA office. This indicates a collaborative office setting designed for team interaction, strategic discussions, and hands-on work, balanced with remote flexibility. Office Location(s):

• San Ramon, CA: This location is a significant business hub in the San Francisco Bay Area, offering access to a vibrant tech ecosystem. Proximity to other tech companies and resources may be beneficial.

Workspace Context:

• Collaborative Environment: The hybrid model and "delivery pod" structure suggest an environment that values in-person collaboration for complex problem-solving, brainstorming architectural solutions, and team alignment.

• Operations Tools & Technology: Access to a robust technology stack, including GCP, Google Gemini Enterprise, and development tools necessary for architecting and operating AI systems. This environment is expected to be technologically advanced and supportive of cutting-edge AI development.

• Operations Team Interaction: Opportunities for close collaboration with a dedicated delivery pod and direct engagement with platform leadership, fostering a dynamic and impactful work experience.

Work Schedule:

• Standard: Typically 40 hours per week.

• Flexibility: While hybrid, the nature of architectural ownership and project delivery may require occasional flexibility in working hours to meet critical deadlines or address urgent operational issues, common in senior technical roles.

📝 Enhancement Note: The hybrid requirement is a key factor for candidates. The San Ramon location is in a competitive tech region. The emphasis on collaboration within a delivery pod and direct interaction with leadership points to a structured yet dynamic work setting.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A review of your resume and portfolio to assess alignment with the core requirements, particularly experience with enterprise AI platforms, multi-agent systems, and architectural ownership.

• Technical Deep Dive: Expect in-depth discussions covering your experience with Google Gemini Enterprise, RAG, identity management, GCP, and Python. This stage will likely involve scenario-based questions and deep dives into your past projects.

• Architectural Design Challenge: You may be presented with a hypothetical AI governance or architecture problem to solve, requiring you to outline your approach, considerations, and proposed solutions, potentially including a whiteboard session or presentation.

• Portfolio Presentation: A dedicated session to walk through key projects from your portfolio, demonstrating your architectural decision-making, problem-solving skills, and impact on previous systems.

• Cultural Fit & Leadership Assessment: Interviews with platform leadership and potentially members of the delivery pod to assess your communication style, leadership approach, and ability to collaborate effectively.

• Final Decision: Based on the cumulative assessment of technical skills, architectural acumen, leadership potential, and cultural fit.

Portfolio Review Tips:

• Quantify Impact: For each project, clearly articulate the problem, your architectural solution, the technologies used, and the quantifiable business impact (e.g., improved reliability by X%, reduced costs by Y%, enabled Z new capabilities).

• Showcase Architectural Ownership: Highlight projects where you were the sole or primary architectural owner, detailing your process for setting standards, making critical decisions, and driving adoption.

• Demonstrate Hands-On AI Expertise: Include examples of designing, building, and operating applied AI systems, specifically mentioning multi-agent systems, RAG, or similar complex AI architectures.

• Detail Governance & Security: Present examples of how you've incorporated governance, security, and identity management into your architectural designs.

• Structure for Clarity: Organize your portfolio logically, perhaps by project type or by the core responsibilities of this role, making it easy for reviewers to find relevant examples.

Challenge Preparation:

• Review Core Technologies: Refresh your knowledge of Google Gemini Enterprise, ADK, RAG concepts, Python, Go, GCP services (Cloud Run, GKE, Vertex AI, IAM), OAuth2, SAML, and RBAC.

• Understand Control Tower Concepts: Familiarize yourself with the principles of centralized governance, registry/gateway operations, retrieval engineering, and observability in the context of AI platforms.

• Practice Problem-Solving: Be prepared to discuss how you would approach designing an architecture for a specific challenge, such as scaling AI agents, ensuring data privacy, or implementing cost controls.

• Articulate Trade-offs: Be ready to discuss the trade-offs inherent in architectural decisions (e.g., performance vs. cost, complexity vs. flexibility).

📝 Enhancement Note: The interview process is designed to rigorously assess senior-level architectural and leadership capabilities. A strong portfolio showcasing direct experience with enterprise AI platforms and governance is paramount. The "architectural owner" aspect will be heavily scrutinized.

🛠 Tools & Technology Stack

Primary Tools:

• Google Gemini Enterprise: Core platform for AI agent development and deployment.

• Google ADK (AI Development Kit): Essential for building applications on Gemini.

• Python: Primary programming language for development and scripting.

• Go (or equivalent): Secondary language proficiency expected for broader development capabilities.

• MCP (Multi-agent Conversation Platform or similar): Experience building servers within this framework.

Analytics & Reporting:

• Observability Tools: Tools for monitoring AI system performance, health, and usage (specifics not named, but likely integrated with GCP or third-party solutions).

• Cost Management Tools: Tools and methodologies for tracking and optimizing token and inference spend (e.g., GCP Cost Management, FinOps practices).

• Reporting Dashboards: Likely utilizing GCP-native tools or BI platforms for visualizing key metrics related to governance, performance, and cost.

CRM & Automation:

• GCP Services:

  • Cloud Run: For containerized application deployment.
  • GKE (Google Kubernetes Engine): For scalable container orchestration.
  • Vertex AI: Google's managed ML platform, likely involved in model training and deployment.
  • Networking Services: For secure and efficient communication.
  • IAM (Identity and Access Management): For access control within GCP.

• Identity & Access Management:

  • OAuth2 / SAML: For authentication and authorization protocols.
  • RBAC (Role-Based Access Control): For granular permission management.

• Retrieval Architecture Components:

  • Vector Stores: Databases designed for storing and querying vector embeddings (e.g., Pinecone, Weaviate, Chroma, or GCP's Vector Search).
  • Embedding Models: For generating vector representations of data.

📝 Enhancement Note: The technology stack is heavily focused on Google Cloud Platform and specific AI development tools. Candidates must demonstrate proficiency not just with individual tools but how they integrate to form a comprehensive AI governance and operational platform. Experience with MCP is a specific requirement.

👥 Team Culture & Values

Operations Values:

• Data-Driven Decision Making: Emphasis on using metrics from observability, cost controls, and governance compliance to inform architectural decisions and operational improvements.

• Reliability & Scalability: A core value is ensuring the AI platform remains robust, performant, and scalable as the number of agents and users grows.

• Security & Compliance: Paramount importance placed on implementing strong identity and access controls, adhering to governance standards, and ensuring the secure operation of AI systems.

• Efficiency & Cost Optimization: A focus on managing resources effectively, optimizing inference and token spend, and driving operational efficiency through automation and smart architecture.

• Collaboration & Ownership: Fostering a culture where individuals take ownership of their domain and collaborate effectively across teams to achieve shared goals.

Collaboration Style:

• Cross-Functional Integration: The role requires extensive collaboration with various application teams, platform leadership, and potentially security and compliance departments to ensure the Control Tower meets diverse needs and integrates seamlessly.

• Process Review & Feedback: An environment that encourages constructive feedback on architectural designs and operational processes, allowing for continuous improvement.

• Knowledge Sharing: Expectation of sharing expertise and best practices across the delivery pod and potentially broader engineering teams to elevate the collective understanding of AI architecture and governance.

📝 Enhancement Note: The culture emphasizes technical excellence, responsibility, and proactive management of complex AI systems. The values align with the core responsibilities of an AI architect focused on governance and operational integrity.

⚡ Challenges & Growth Opportunities

Challenges:

• Balancing Innovation with Governance: Navigating the inherent tension between rapid AI innovation and the need for stringent governance, security, and cost controls.

• Scaling Complex Systems: Architecting and operating a system that can reliably handle a rapidly growing number of AI agents and diverse use cases.

• Technical Debt Management: Proactively identifying and mitigating technical debt in a fast-evolving AI landscape.

• Cross-Team Alignment: Ensuring buy-in and adherence to architectural standards across multiple development teams with potentially competing priorities.

• Cost Management at Scale: Effectively managing and optimizing significant cloud and AI service expenditures.

Learning & Development Opportunities:

• AI Architecture Specialization: Deepen expertise in advanced AI patterns, multi-agent systems, and enterprise-grade AI platform design.

• Cloud Platform Mastery: Enhance skills with Google Cloud Platform services, particularly those related to AI and infrastructure management.

• Leadership & Strategy: Develop strategic thinking and leadership capabilities through direct engagement with senior management and team leadership.

• Industry Best Practices: Stay at the forefront of AI governance, security, and operational best practices through continuous learning and application.

• Mentorship: Opportunities to mentor junior engineers within the delivery pod and learn from experienced platform leaders.

📝 Enhancement Note: The challenges are typical of senior roles in rapidly evolving tech fields, particularly AI. The growth opportunities are focused on deepening specialized technical expertise and developing leadership acumen within the AI/ML platform domain.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you were the sole architectural owner of an enterprise platform. What were your key responsibilities, how did you set standards, and how did you ensure adoption?" (Focus on ownership, standard-setting, and enforcement.)

• "How would you design a reusable top-level agent layer for a multi-agent system to handle context, retrieval, and synthesis efficiently and securely?" (Prepare to sketch out an architecture, discuss trade-offs, and detail components like orchestrators and retrieval mechanisms.)

• "Discuss your experience with RAG architecture. What are the critical components, and what are common pitfalls to avoid when implementing it at scale?" (Be ready to explain embedding strategies, vector stores, chunking, and hybrid search.)

• "Explain how you would implement robust identity and entitlement enforcement for AI agents accessing sensitive data. What protocols and access control models would you consider?" (Focus on OAuth2, SAML, RBAC, and data-level ACL mapping.)

• "Imagine the AI platform's token and inference costs are escalating rapidly. What steps would you take to diagnose the issue and implement cost controls?" (Prepare to discuss monitoring, optimization strategies, and FinOps principles.) Company & Culture Questions:

• "What interests you about Zensar and this specific role as an AI Architect for a Governance Control Tower?" (Research Zensar's AI initiatives and align your interests with their goals.)

• "How do you approach translating complex technical requirements from leadership into actionable architectural plans for your team?" (Highlight your communication and planning methodologies.)

• "Describe your experience leading a cross-functional delivery pod. How do you ensure accountability and maintain a high quality bar?" (Focus on leadership, review processes, and team management.) Portfolio Presentation Strategy:

• Select High-Impact Projects: Choose 2-3 projects that best demonstrate your experience as an architectural owner of complex systems, ideally involving AI, governance, or scalable cloud deployments.

• Structure Your Narrative: For each project, use the STAR method (Situation, Task, Action, Result), emphasizing your specific actions and the quantifiable results achieved.

• Detail Architectural Decisions: Clearly explain why you made certain architectural choices, discussing trade-offs, alternatives considered, and the rationale behind your decisions.

• Highlight Governance & Operations: Specifically call out how you incorporated governance, security, and operational considerations into your designs.

• Be Prepared for Technical Deep Dives: Anticipate detailed questions about the technologies, challenges, and solutions presented in your portfolio.

📝 Enhancement Note: Interview preparation should focus on demonstrating senior-level architectural thinking, hands-on technical depth, and leadership experience in complex AI environments. Candidates must be ready to articulate their thought processes and defend their architectural decisions.

📌 Application Steps

To apply for this operations position:

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

• Customize Your Resume: Tailor your resume to highlight your 10+ years of software engineering and architecture experience, with a specific emphasis on your 3+ years in applied AI systems, enterprise platform ownership, and experience with Google Gemini Enterprise, RAG, and GCP. Use keywords from the job description.

• Prepare Your Portfolio: Curate 2-3 key projects that showcase your architectural design skills, hands-on AI implementation experience, and success in roles involving governance, control, or operational ownership. Ensure each project clearly articulates the problem, your solution, the technologies used, and quantifiable results.

• Practice Your Pitch: Rehearse your portfolio walkthrough and be ready to discuss your experience with the core technologies and responsibilities outlined in the job description, focusing on your strategic approach and problem-solving abilities.

• Research Inabia & Zensar: Understand Inabia's role as a consulting and staffing firm and Zensar's position as a global technology services company. Familiarize yourself with Zensar's offerings, particularly in AI and digital transformation, to better align your responses during interviews.

⚠️ 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. The "Gemini AI Architect" role is highly specialized and requires extensive experience in AI architecture and governance.