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FOR 10+ YEAR TECHNOLOGY PROFESSIONALS

Become the Architect Who Gives Enterprise AI
a Production-Ready Foundation

Move from contributing to isolated AI use cases to designing the platforms, standards and guardrails that help multiple teams build AI systems safely and at scale.

You already understand software systems, integration, cloud and production constraints. This mentorship helps you extend that experience into enterprise GenAI architecture, covering LLM platforms, RAG, agents, security, governance, evaluation, observability and technical leadership.

Build My GenAI Architect Roadmap Explore the Architect Roadmap
Next cohort starts: Sep 05, 2026
Live Cohort
Seats: 30

Does Any of This Feel Familiar?

Your organisation is launching AI pilots, but every team is choosing its own models, frameworks and patterns.

You can build individual AI features, but you are not yet confident defining an enterprise-wide architecture.

Leadership asks for an AI strategy, while engineering teams need decisions about data, security, evaluation and deployment.

You are expected to review AI designs, but the ecosystem changes faster than traditional architecture playbooks.

You can explain microservices and cloud platforms, but agentic AI, model risk and LLMOps require a new architecture lens.

You do not want to become the architect who only draws boxes after the decisions have already been made.

You want to lead AI direction without becoming disconnected from implementation reality.

You do not need another list of tools. You need a repeatable way to make and defend architecture decisions.

You do not need to abandon your architecture experience.
You need to extend it into enterprise AI systems, governance and technical leadership.

Built for Experienced Professionals Ready to Lead AI Architecture

If you have spent years designing, building, integrating, deploying or governing production systems, this program helps you apply that experience to enterprise GenAI platforms and operating models.

This is not a framework course. It is an architecture and technical leadership path.

Participants may come from Java, C#, C++, Kotlin, Python, Go, Scala or another modern software engineering ecosystem. The programming language is not the centre of the page; architecture judgement is.

Senior engineers preparing for architecture roles
Solution and application architects
Enterprise and platform architects
Cloud architects working on AI-enabled platforms
AI Engineers ready to move into architecture
Technical leads, engineering managers and consultants responsible for enterprise AI decisions

What Does a GenAI Architect Actually Do?

The role connects business strategy, AI use cases, data, models, applications, platforms, security, governance, operations and delivery teams. A credible GenAI Architect is not just the most senior developer, not a cloud architect adding an LLM API box, and not someone who recommends tools without understanding operating constraints.

Understand enterprise goals and constraints
Define architecture principles
Choose platform and integration patterns
Establish security, evaluation and governance
Guide implementation across teams
Review production readiness
Measure, learn and evolve the platform

You are not only designing one AI application. You are designing the foundation that allows many AI applications to be built, governed and operated responsibly.

An AI Engineer builds the system. An FDE drives the customer outcome. A GenAI Architect defines the technical foundation, standards and guardrails that allow systems and teams to scale.

Career Transformation Outcomes

Design Enterprise AI Platforms

Create target-state and reference architectures for LLM applications, RAG systems, agent platforms, integration, evaluation and operations.

Establish Standards and Guardrails

Define architecture principles for security, governance, model use, data access, evaluation, observability, cost and production readiness.

Lead Technical Direction

Guide engineering teams, challenge vendor claims, communicate trade-offs to leadership and become a credible design authority for enterprise AI.

Complete GenAI Architect Curriculum

A senior architecture roadmap for enterprise AI platforms, governance, operating models and technical leadership.

Module 1 - The GenAI Architect Role and Architecture Mindset

Move from designing individual applications to shaping enterprise AI direction.

  • - GenAI Architect responsibilities
  • - AI Engineer versus FDE versus GenAI Architect
  • - Solution, platform and enterprise architecture perspectives
  • - Architecture principles
  • - Quality attributes
  • - System boundaries
  • - Architecture decision-making under uncertainty
  • - Functional and non-functional requirements
  • - Architecture fitness criteria
  • - Technical debt in AI systems
  • - Architecture documentation
  • - Architecture decision records
  • - Communicating assumptions and trade-offs
Module 2 - Enterprise GenAI Reference Architecture

Understand the complete set of capabilities required to run GenAI safely in an enterprise.

  • - Experience and application layer
  • - API and orchestration layer
  • - Model gateway
  • - Foundation model providers
  • - Model routing
  • - Prompt and configuration management
  • - RAG and enterprise knowledge layer
  • - Agent orchestration
  • - Tool and integration layer
  • - Identity and access
  • - Evaluation
  • - Guardrails
  • - Observability
  • - Governance
  • - Cost management
  • - Data and analytics
  • - Platform engineering
  • - Deployment environments
  • - Human-in-the-loop controls
  • - Shared versus application-specific capabilities
Module 3 - LLM Platform and Model Strategy

Choose and govern models based on enterprise constraints rather than vendor hype.

  • - Hosted versus self-hosted models
  • - Commercial versus open models
  • - Model selection criteria
  • - Quality, latency, cost and privacy trade-offs
  • - Model gateways
  • - Model routing and fallback
  • - Multi-model strategies
  • - Model lifecycle
  • - Version changes
  • - Context-window constraints
  • - Structured outputs
  • - Tool calling
  • - Multimodal patterns
  • - Fine-tuning versus prompting versus RAG
  • - Model contracts and abstraction
  • - Vendor dependency and lock-in
  • - Build-versus-buy decisions
Module 4 - Enterprise RAG and Knowledge Architecture

Design trusted enterprise knowledge systems with security, relevance and lifecycle controls.

  • - Source-system integration
  • - Content ingestion architecture
  • - Document lifecycle
  • - Chunking and embedding strategies
  • - Vector and hybrid retrieval
  • - Metadata
  • - Reranking
  • - Query transformation
  • - Knowledge freshness
  • - Citation and source traceability
  • - Access-controlled retrieval
  • - Tenant and domain isolation
  • - Sensitive information handling
  • - Retrieval evaluation
  • - RAG observability
  • - Enterprise search integration
  • - Knowledge graphs where appropriate
  • - Multi-domain knowledge architecture
  • - Failure and fallback patterns
Module 5 - Agentic AI and Enterprise Workflow Architecture

Design controlled agent systems that can act without creating unacceptable operational risk.

  • - Agent versus workflow decisions
  • - Single-agent and multi-agent patterns
  • - Planner-executor patterns
  • - State and memory
  • - Tool registries
  • - Permissions
  • - Human approval
  • - Long-running workflows
  • - Event-driven agents
  • - Failure recovery
  • - Idempotency
  • - Auditability
  • - Agent observability
  • - Agent security boundaries
  • - OpenAI Agents SDK
  • - LangGraph
  • - CrewAI
  • - Google ADK
  • - LangChain where appropriate
  • - Framework trade-offs
  • - Protocol and interoperability considerations
  • - Avoiding unnecessary agent complexity

Architecture patterns and framework-selection criteria matter more than tool memorisation.

Module 6 - Evaluation, Guardrails and Responsible AI Architecture

Define how the enterprise determines whether AI systems are useful, safe and production-ready.

  • - Evaluation strategy
  • - Offline and online evaluation
  • - Golden datasets
  • - Retrieval metrics
  • - Response-quality metrics
  • - LLM-as-judge patterns and limitations
  • - Task-specific evaluation
  • - Regression testing
  • - Red-team testing
  • - Prompt injection
  • - Data leakage
  • - Toxicity and policy controls
  • - Hallucination risk
  • - Human oversight
  • - Explainability requirements
  • - Responsible AI principles
  • - Approval and exception workflows
  • - Audit trails
  • - Production readiness criteria
  • - Continuous evaluation
Module 7 - Security, Privacy and AI Governance

Build enterprise trust through enforceable controls rather than policy documents alone.

  • - AI threat modelling
  • - Prompt injection and tool abuse
  • - Identity and access management
  • - Least-privilege tool access
  • - Data classification
  • - Sensitive data controls
  • - Encryption and secrets
  • - Tenant isolation
  • - Model-provider data handling
  • - Logging and retention
  • - Privacy
  • - Compliance considerations
  • - Responsible AI governance
  • - Model and use-case inventory
  • - Risk classification
  • - Architecture review gates
  • - Policy enforcement
  • - Auditability
  • - Incident response
  • - Third-party and vendor risk
Module 8 - LLMOps, MLOps and Platform Operations

Design the operating model required to deploy, observe and evolve AI systems across teams.

  • - AI platform operating model
  • - Development, test and production environments
  • - CI/CD
  • - Infrastructure as code
  • - Prompt and configuration versioning
  • - Model version management
  • - Experiment tracking where relevant
  • - Deployment strategies
  • - Model gateway operations
  • - Tracing
  • - Observability
  • - Quality monitoring
  • - Cost monitoring
  • - Latency monitoring
  • - Reliability
  • - Scaling
  • - Quotas and rate limits
  • - Incident management
  • - Rollback and fallback
  • - Platform service levels
  • - MLOps foundations
  • - LLMOps practices
  • - Shared platform ownership
  • - FinOps for AI workloads
Module 9 - Cloud, Integration and Enterprise Systems Architecture

Integrate GenAI into the real enterprise rather than building isolated AI islands.

  • - API-first integration
  • - Event-driven architecture
  • - Workflow engines
  • - Enterprise service integration
  • - Data platforms
  • - Existing applications
  • - Identity systems
  • - SaaS and vendor platforms
  • - Hybrid and multi-cloud considerations
  • - Network boundaries
  • - Private connectivity
  • - Deployment topologies
  • - Multi-tenancy
  • - Scalability
  • - Resilience
  • - Disaster recovery considerations
  • - Edge and regional requirements where relevant
  • - Integration governance
  • - Reusable enterprise services
Module 10 - Architecture Leadership and Organisational Adoption

Become the person who can align executives, architects and delivery teams around a credible AI direction.

  • - AI strategy translated into architecture
  • - Capability roadmaps
  • - Architecture governance
  • - Design authority
  • - Architecture review boards
  • - Guiding multiple teams
  • - Technical standards
  • - Reference implementations
  • - Platform versus product team responsibilities
  • - Build-versus-buy communication
  • - Vendor evaluation
  • - Executive communication
  • - Communicating risk and uncertainty
  • - Prioritisation
  • - Stakeholder alignment
  • - Coaching engineers and architects
  • - Organisational operating model
  • - AI centre-of-excellence considerations
  • - Balancing central governance with team autonomy
Module 11 - Enterprise Capstone: GenAI Platform and Governance Blueprint

Create and defend an enterprise AI architecture that could guide real implementation.

  • - Business and technology context
  • - Current-state assessment
  • - Target-state architecture
  • - Architecture principles
  • - Reference architecture
  • - Platform capability map
  • - Model strategy
  • - RAG strategy
  • - Agent strategy
  • - Security architecture
  • - Evaluation strategy
  • - Governance model
  • - LLMOps and MLOps operating model
  • - Integration plan
  • - Cost and scale considerations
  • - Architecture risks
  • - Adoption roadmap
  • - Architecture decision records
  • - Executive presentation
  • - Technical design defence
  • - Portfolio case study

What You Will Design - Not Just Discuss

Architecture Artifacts

  • - Enterprise GenAI reference architecture
  • - Target-state architecture
  • - Platform capability map
  • - Model gateway and routing design
  • - Enterprise RAG architecture
  • - Agent platform architecture
  • - Integration architecture
  • - Deployment topology

Governance and Operating Artifacts

  • - Architecture principles
  • - AI use-case classification
  • - Security and risk controls
  • - Evaluation strategy
  • - Responsible AI governance model
  • - Architecture review checklist
  • - Production-readiness criteria
  • - LLMOps operating model
  • - Cost and observability framework

Leadership Artifacts

  • - AI capability roadmap
  • - Build-versus-buy decision
  • - Vendor evaluation framework
  • - Architecture decision records
  • - Executive architecture presentation
  • - Cross-team technical standards
  • - Architecture review narrative

The value of a GenAI Architect is not the number of diagrams created. It is the quality of decisions those diagrams make possible.

What You Will Be Ready to Show After the Program

Architecture Evidence

  • - An enterprise GenAI reference architecture
  • - A platform capability model
  • - RAG and agent architecture patterns
  • - Security and governance controls
  • - Evaluation and observability strategy
  • - LLMOps operating model
  • - Technical decision records
  • - An implementation roadmap

Career Evidence

  • - A credible GenAI Architect portfolio case study
  • - Interview-ready architecture stories
  • - Stronger architecture-review responses
  • - A revised LinkedIn and resume narrative
  • - Evidence of leading cross-team technical decisions
  • - A stronger internal-promotion or architecture-role story
  • - The ability to communicate AI trade-offs to engineers and leadership

The goal is not to say, "I know GenAI tools." The goal is to say, "I can design the architecture, standards and operating model required to scale GenAI safely across an enterprise."

Program Delivery and Support

6-month architecture mentorship
Live cohort
Architecture clinics
Design reviews
Architecture case studies
Whiteboard sessions
Platform-design exercises
Capstone and career narrative feedback

Why This Is Not Another GenAI Course

Architecture Before Tools

Frameworks and vendors change. Architecture principles and decision-making remain.

Enterprise Systems Before Isolated Demos

The focus is the platform, integration, security and operating model required beyond the prototype.

Governance That Can Be Implemented

Security, evaluation and responsible AI are designed into the architecture rather than added as policy slides later.

Technical Leadership Before Title Chasing

The goal is to develop the judgement and evidence required to lead architecture, not simply to claim the title.

Sanjay Negi

Learn Architecture From Someone Who Has Lived the Full Software Lifecycle

I spent more than two decades working across enterprise software, from requirements, design and development to testing, deployment, monitoring, incidents and production support.

That experience taught me that architecture is not about drawing boxes. It is about making decisions that survive implementation, production pressure and organisational constraints.

When I moved into GenAI, I found plenty of tool tutorials but very little guidance on how experienced professionals should design complete AI systems, platforms and operating models.

This mentorship brings those worlds together: deep software lifecycle experience, modern GenAI systems and architecture-first technical leadership.

Commitment Policy and Success Support

What this requires

This is a small working cohort. You should expect architecture exercises, design-review participation, case-study preparation, capstone completion, consistent weekly effort, feedback and iteration.

This is not passive certificate-only participation.

What this does not promise

This is not a placement-guarantee program. The mentorship helps you build the architecture capability, portfolio evidence and professional narrative required for GenAI Architect opportunities.

It does not guarantee promotion, employment or a specific title.

Standard FAQs

Straight answers for experienced professionals evaluating the GenAI Architect path.

Who is this mentorship designed for?
It is designed for senior engineers, technical leads, solution architects, enterprise architects, platform architects, cloud architects, AI Engineers and technically involved managers who want to lead enterprise GenAI architecture.
Is it suitable if I come from Java, C#, C++, Kotlin, Python, Go, Scala or another language?
Yes. Your production software and systems experience matters more than one language. Python and AI Engineering concepts appear where needed, but architecture judgement is the focus.
Do I need previous AI or machine-learning experience?
Hands-on AI experience is useful, but the program is designed to help experienced technology professionals build the architecture lens for GenAI systems. It is not an ML research curriculum.
How much Python do I need before joining?
You should understand software engineering and be willing to work through practical AI examples. You do not need to be a Python specialist to learn architecture patterns, trade-offs and operating models.
Is this a beginner course?
No. It assumes substantial software, platform, architecture or enterprise systems experience.
Is this suitable for someone with fewer than 10 years of experience?
The primary audience is 10+ year professionals. A strong technologist with fewer years may be a fit if they have serious production and architecture exposure.
Will I build real projects or architecture artifacts?
Yes. You will create reference architectures, platform capability maps, governance artifacts, decision records and an enterprise capstone blueprint.
Will I work on a deployable AI architecture?
Yes. The capstone is designed as an enterprise AI architecture and governance blueprint that could guide real implementation.
Is the program live or recorded?
The existing page identifies this as a live cohort. The value comes from live design thinking, review and capstone feedback.
Will recordings be available?
Yes. While we strongly encourage you to attend every live session because that's where you'll benefit from discussions, Q&A, and architecture reviews, you'll also receive lifetime access to the session recordings. If you miss a session or want to revisit a concept later, the recordings will always be available.
What happens if I miss a live session?
Life happens, especially when you're balancing a demanding job. If you miss a session, you can catch up using the lifetime recordings. However, we strongly recommend attending live whenever possible, as the discussions, project reviews, and Q&A sessions provide additional value that recordings cannot fully capture.
How much time should I set aside each week?
The live sessions are designed to help you understand the concepts and build working solutions together. To get the maximum value from the mentorship, plan to spend another 4-6 hours each week implementing what you've learned, experimenting with the code, reviewing the material, and progressing on your projects. The more you build, the more confident you'll become.
Is the program designed for working professionals?
Yes. The page is designed for experienced Indian working professionals with software, architecture or enterprise systems backgrounds.
What kind of mentor and project support will I receive?
Every live session includes opportunities to ask questions and discuss implementation challenges. In addition, if participants need extra help with their projects, architecture, or technical roadblocks, additional weekend mentoring or project review sessions may be scheduled whenever required. The goal is to help you successfully complete your project, not leave you struggling alone.
Will this help with interviews, internal role changes, promotions and appraisals?
It is designed to help you build architecture evidence, decision narratives and stronger responses for architecture reviews, interviews and internal mobility. It does not guarantee outcomes.
Do you provide placement or guarantee an architect role?
No. This is not a placement-guarantee program. The mentorship helps you build the architecture capability, portfolio evidence and professional narrative required for GenAI Architect opportunities.
Will I receive a certificate?
Yes. You'll receive a certificate on successful completion of the mentorship.

That said, a certificate should never be your primary reason for joining. Employers are far more interested in your ability to demonstrate what you've built. Your deployed AI system, architecture diagrams, project portfolio, and ability to explain your technical decisions will create much stronger career opportunities than a certificate alone.
What makes this different from Udemy, Coursera, YouTube or self-paced architecture programs?
Self-paced content can explain topics. This path is organised around architecture decisions, governance, platform thinking, review and capstone evidence.
I have already purchased AI courses. Why should I consider this?
If those courses taught tools but not enterprise architecture decisions, this mentorship helps you create repeatable architecture patterns, governance and operating models.
What happens after the mentorship ends?
By the end of the mentorship, you should have a much stronger GenAI Architect identity, a portfolio of real work, and a clear roadmap for continuing your learning and career growth.

If you need guidance after the program, you can always reach out to us at support@sanjaynegi.in or connect with Sanjay on LinkedIn. We'll do our best to help you move forward whenever possible.
Can my employer sponsor the program?
Employer sponsorship may be possible depending on your organisation. Use the application process to clarify payment and documentation requirements.
How are participants selected?
These mentorships are designed for working professionals with significant software engineering experience who are serious about building AI capabilities.

There is no competitive entrance test. Before enrolling, we simply expect you to review the program carefully and ensure it aligns with your background, goals, and commitment level. If you're unsure whether the program is the right fit, feel free to contact us before registering.
What is the fee and what does it include?
The current program fee is displayed during the registration process.

The fee covers the complete live mentorship, session recordings, learning resources, project guidance, architecture reviews, and community access included with your chosen program.
Are instalment options available?
If you'd like to explore instalment options or have any payment-related questions, please write to support@sanjaynegi.in with the name of the program you're interested in. We'll let you know the options available.
Can I speak to someone before making a decision?
We recommend reviewing the program curriculum and FAQs first to ensure it aligns with your goals.

If you still have questions or would like clarification before enrolling, feel free to write to support@sanjaynegi.in. We'll be happy to guide you and help you determine whether the program is the right fit for you.
What if I don't know Python?
You don't need to be a Python expert before joining. If you've spent years building software in Java, C#, C++, Kotlin, Go, or another programming language, you'll find Python straightforward to pick up. We'll teach the Python required for AI Engineering as part of the program, so you can focus on learning AI concepts rather than worrying about the language.

GenAI Architect-Specific FAQs

How the senior architecture path works in practice.

What exactly does a GenAI Architect do?
A GenAI Architect defines the technical foundation, standards, governance and operating model that help multiple teams build AI systems safely and at scale.
How is a GenAI Architect different from an AI Engineer?
An AI Engineer builds and deploys production AI systems. A GenAI Architect defines platform architecture, standards, governance and technical direction across systems and teams.
How is a GenAI Architect different from a Forward Deployed Engineer?
An FDE works with customers to discover the right problem and drive production adoption. A GenAI Architect creates the enterprise foundation that many teams and use cases can build on.
How is this different from a traditional solution or enterprise architecture program?
It adds GenAI-specific model strategy, RAG, agents, evaluation, LLMOps, governance, security and responsible AI architecture to traditional architecture judgement.
Do I need to be an architect already?
No. You should have substantial production software or enterprise systems experience, but you do not need to already hold an architect title.
Do I need hands-on AI Engineering experience before joining?
Hands-on AI experience is useful but not always mandatory. The path remains technically grounded and helps you understand implementation deeply enough to guide and challenge teams.
Will I still write code?
Architects should understand implementation reality. You may work through technical examples, but the main output is architecture, governance and operating-model evidence.
How much LLM Engineering depth is included?
Enough to make model, gateway, routing, context, cost, latency, privacy and reliability decisions credibly.
Will we cover enterprise RAG architecture?
Yes. Enterprise RAG architecture is a dedicated module covering ingestion, retrieval, access control, tenant isolation, evaluation and observability.
Will we cover agentic AI architecture?
Yes. Agentic AI is covered through agent versus workflow decisions, tool permissions, human approval, auditability, observability, failure recovery and enterprise risk.
Will OpenAI Agents SDK, LangGraph, CrewAI, Google ADK and LangChain be included?
Yes, these are represented as implementation tools and architecture options. Frameworks are not the centre of the role.
Will we cover MLOps and LLMOps?
Yes. The platform operations module covers LLMOps practices, MLOps foundations, deployment, observability, quality, cost, latency, reliability and incident management.
Will we cover security, governance and Responsible AI?
Yes. Security, privacy, responsible AI governance, model and use-case inventory, risk classification, review gates, auditability and incident response are central.
Will we cover architecture on cloud platforms?
Yes. Cloud, hybrid and multi-cloud considerations, private connectivity, deployment topologies, scalability and resilience are included at an architecture level.
Will we cover model and vendor selection?
Yes. The curriculum covers hosted versus self-hosted, commercial versus open models, model gateways, routing, fallback, vendor lock-in and build-versus-buy decisions.
Will we cover build-versus-buy decisions?
Yes. Build-versus-buy decisions appear in model strategy, platform strategy, vendor evaluation and executive communication.
Will we create reference architectures and architecture decision records?
Yes. Reference architectures, architecture decision records and design-review narratives are core artifacts.
Is this suitable for engineering managers and technical leads?
Yes, especially if you remain close to architecture and want to guide teams through GenAI decisions, standards and trade-offs.
Will this help me prepare for architecture reviews and interviews?
Yes. You will build architecture artifacts, trade-off narratives and capstone evidence that can support architecture reviews, interviews and internal role conversations.
What kind of enterprise capstone will I complete?
You will create and defend a GenAI platform and governance blueprint covering target-state architecture, model strategy, RAG, agents, security, evaluation, governance, LLMOps, integration, cost, risk and adoption roadmap.

Check Whether the GenAI Architect Path Fits You

Suitable if

  • - You have substantial experience with software or enterprise systems
  • - You understand production constraints
  • - You want to move from feature-level decisions to system-level decisions
  • - You want to guide multiple engineers or teams
  • - You are willing to develop stronger AI Engineering depth
  • - You want to make and defend architecture trade-offs
  • - You want to work across technology, security, data and business constraints
  • - You can commit time to architecture exercises and capstone work
  • - You want technical leadership rather than only a new title

Not suitable if

  • - You have no software engineering or systems background
  • - You want only prompt-engineering or AI-tool demonstrations
  • - You want a non-technical executive-awareness program
  • - You want a pure data-science or ML-research program
  • - You do not want to understand implementation details
  • - You expect guaranteed promotion or placement
  • - You want a passive recorded course
  • - You are only collecting certificates
  • - You want to call yourself an architect without doing architecture work

Move From Building Individual AI Features
to Shaping Enterprise AI Direction

Build the architecture judgement, platform perspective and leadership evidence required to design GenAI systems that multiple teams can trust, govern and scale.

Build My GenAI Architect Roadmap

Application-based entry · Built for experienced professionals · Small working cohort