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FOR 10+ YEAR SOFTWARE PROFESSIONALS

Your Engineering Experience Should Become
Your Advantage in the AI Era

Move from maintaining traditional software systems to designing, building and deploying production-ready AI systems.

You already understand software design, APIs, databases, integration, cloud and production pressure. This mentorship helps you apply that experience to LLM engineering, RAG, agentic AI and LLMOps without treating you like a beginner.

Rewrite My AI Career Story Explore the AI Engineer Roadmap
Starts August 2, 2026
Live Weekend Cohort
Saturday & Sunday · 10:00 AM-12:00 PM IST
Limited to 30 Working Professionals

Does Any of This Feel Familiar?

Your company is talking about AI-first delivery, but the important AI projects keep going to another team.

You have completed courses and tutorials, but you still cannot confidently design an end-to-end AI system.

You understand production software, but AI learning paths keep treating you like a fresher.

You can build APIs and enterprise applications, but RAG, agents, evaluations and LLMOps still feel disconnected.

You do not need another certificate. You need a structured transition and a system you can defend in an architecture review.

You are not afraid of learning. You are afraid of spending another year learning the wrong things.

You do not need to discard your experience.
You need to extend it into AI Engineering.

Built for Experienced Software Professionals

If you have spent years designing, building, integrating, deploying or supporting production software, your experience is relevant. Your background may be Java, C#, C++, Kotlin, Python, Go, Scala or another modern software engineering ecosystem.

This program is built around engineering judgement, not one specific programming language.

This is not a beginner AI course, prompt-engineering workshop, data-science program or certificate-focused bootcamp.

Senior software engineers and technical leads
Backend and platform engineers
Solution and application architects
Engineering managers who remain technically involved
Cloud and infrastructure engineers with software development experience
Technical consultants who implement enterprise systems

What Does a Production-Ready AI Engineer Actually Do?

An AI Engineer does more than call an LLM API. The role connects product context, software architecture, data, retrieval, model behaviour, evaluation, deployment and production feedback into one working system.

Understand the use case
Design the architecture
Build the AI application
Ground it with enterprise knowledge
Add evaluation and guardrails
Deploy and observe it
Improve it using production feedback

You are not learning to create impressive demos. You are learning to build AI systems that survive contact with real users, real data and real production constraints.

Career Transformation Outcomes

Build Production AI Systems

Design and implement LLM, RAG and agentic applications that solve real operational or business problems.

Defend Engineering Decisions

Explain model choices, retrieval strategies, orchestration patterns, security controls, evaluation methods and deployment decisions.

Lead AI Implementation

Move from being assigned isolated coding tasks to becoming someone who can shape and deliver an end-to-end AI initiative.

Complete AI Engineer Curriculum

An architecture-first path from experienced software professional to production-ready AI Engineer.

Module 1 - AI Engineering Foundations for Experienced Developers

Connect your existing software engineering experience to the AI application stack.

  • - Python required for AI Engineering
  • - API design and integration
  • - LLM fundamentals
  • - Tokens, context windows and model constraints
  • - Prompt design patterns
  • - Structured outputs
  • - Tool and function calling
  • - Responsible AI foundations
  • - Git and development workflow for AI applications
Module 2 - LLM Engineering

Build reliable applications around modern foundation models.

  • - Working with OpenAI, Anthropic and Gemini APIs
  • - Model selection and trade-offs
  • - Context engineering
  • - Streaming responses
  • - Structured generation
  • - Tool calling
  • - Multimodal application patterns
  • - Model fallback and routing
  • - Cost, latency and reliability considerations
  • - Model Context Protocol concepts and integration patterns where relevant
Module 3 - RAG and Enterprise Knowledge Systems

Build AI systems grounded in trusted organisational knowledge.

  • - Document ingestion
  • - Chunking strategies
  • - Embeddings
  • - Vector databases
  • - Metadata filtering
  • - Semantic and hybrid retrieval
  • - Reranking
  • - Query transformation
  • - Context assembly
  • - Citations and source attribution
  • - Retrieval evaluation
  • - Enterprise RAG architecture patterns
  • - Access control and document security considerations
Module 4 - Agentic AI Engineering

Build AI systems that can reason, use tools and execute controlled workflows.

  • - Agent design patterns
  • - Single-agent and multi-agent systems
  • - State and memory
  • - Planning and execution
  • - Human-in-the-loop workflows
  • - Tool orchestration
  • - Error handling and recovery
  • - OpenAI Agents SDK
  • - LangGraph
  • - CrewAI
  • - Google ADK
  • - LangChain where appropriate
  • - Framework selection and trade-offs
  • - Avoiding unnecessary agent complexity

Frameworks are implementation options. The focus is architecture, trade-offs and production delivery above framework memorisation.

Module 5 - Evaluation, Safety and Guardrails

Know whether an AI system is reliable before users or customers depend on it.

  • - LLM evaluation fundamentals
  • - Golden datasets
  • - Retrieval evaluation
  • - Response-quality evaluation
  • - LLM-as-judge patterns and limitations
  • - Hallucination analysis
  • - Prompt injection and data leakage risks
  • - Content and policy guardrails
  • - Human review patterns
  • - Regression testing for AI applications
  • - Responsible AI considerations
Module 6 - LLMOps, MLOps and Production Readiness

Deploy, observe and improve AI systems in real production environments.

  • - FastAPI or an equivalent service layer
  • - Docker and containerisation
  • - CI/CD for AI applications
  • - Environment and secrets management
  • - Cloud deployment
  • - Model and prompt versioning
  • - Experiment tracking where relevant
  • - Monitoring
  • - Tracing
  • - Observability
  • - Latency and cost monitoring
  • - Reliability and fallback patterns
  • - Security
  • - Scalability
  • - MLOps foundations relevant to AI Engineering
  • - LLMOps workflows
  • - Production incident thinking
Module 7 - Architecture, Integration and Enterprise Delivery

Integrate AI into existing systems instead of treating it as an isolated prototype.

  • - AI application architecture patterns
  • - Integration with existing APIs and databases
  • - Event-driven and workflow-based integration
  • - Authentication and authorisation
  • - Multi-tenancy considerations
  • - Data privacy
  • - Cloud architecture
  • - Build-versus-buy decisions
  • - Architecture documentation
  • - Technical design reviews
  • - Production readiness reviews
Module 8 - Capstone: Deploy a Production-Ready AI System

Turn your learning into a system and career story you can present with confidence.

  • - Use-case selection
  • - Problem definition
  • - Architecture design
  • - Implementation
  • - Evaluation
  • - Deployment
  • - Monitoring
  • - Technical documentation
  • - Architecture diagram
  • - Business-impact narrative
  • - Demo and defence
  • - Portfolio case study
  • - Interview and appraisal positioning

What You Will Build - Not Just Watch

A production-style LLM application with structured outputs and tool calling
An enterprise RAG system grounded in real documents
An agentic workflow that performs a controlled multi-step business process
An evaluation pipeline for retrieval and answer quality
A deployed AI service with monitoring, tracing and guardrails
A capstone AI system with architecture documentation and business-impact narrative

Exact capstones may vary according to participant background, workplace context and chosen use case.

What You Will Be Ready to Show After the Program

Technical Evidence

  • - A deployed AI system
  • - Architecture diagrams
  • - Source-controlled implementation
  • - Evaluation results
  • - Deployment and observability setup
  • - Design decisions and trade-offs
  • - Technical documentation

Career Evidence

  • - An AI Engineering portfolio case study
  • - A stronger appraisal narrative
  • - Interview-ready architecture explanations
  • - A revised LinkedIn and resume story
  • - Evidence of moving from course consumption to system delivery

The goal is not to say, "I completed a GenAI course." The goal is to say, "I designed, evaluated and deployed an AI system - and I can defend every major decision."

Program Delivery and Support

6-month structured mentorship
Live weekend cohort
Saturday and Sunday, 10:00 AM-12:00 PM IST
Architecture reviews
Code feedback
Project clinics
Production-thinking reviews
Career narrative support

Why This Is Not Another AI Course

Architecture Before Frameworks

Frameworks change. Engineering judgement remains.

Production Before Certificates

The objective is a deployed system, not another completion badge.

Mentorship Before Content Overload

Participants receive guidance, review and feedback instead of only consuming recorded lessons.

Career Transformation Before Tool Collection

The program helps experienced engineers develop a credible AI Engineering identity and body of work.

Sanjay Negi

Learn From Someone Who Has Made the Same Transition

I spent more than two decades building and supporting enterprise software across design, development, testing, deployment, monitoring and production support.

When GenAI began changing the industry, I did what many experienced engineers do: I bought courses, followed tutorials and collected disconnected knowledge. But none of it gave me a clear path from software engineering to production AI systems.

I rebuilt that path around architecture, implementation, deployment and engineering judgement.

This mentorship gives experienced professionals the structured transition I wish I had when I started.

Commitment Policy and Success Support

What this requires

This is a small live cohort for working professionals. You should expect to participate actively, complete project work, ask for feedback and use the mentorship to build a defensible AI Engineering body of work.

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 technical capability, deployed work and professional narrative required for AI Engineering opportunities.

Employment outcomes still depend on your work, market context, interview performance and role fit.

Standard FAQs

Straight answers for experienced professionals evaluating the AI Engineer path.

Who is this mentorship designed for?
It is designed for experienced software professionals, senior developers, technical leads, architects, technically involved engineering managers, platform engineers and implementation consultants who want to move into production AI Engineering work.
Is it suitable if I come from Java, C#, C++, Kotlin, Python, Go, Scala or another language?
Yes. The program is not centred on one language. Your object-oriented design, API, integration, testing, deployment and production experience matters. You will still need to become comfortable with Python for AI Engineering workflows.
Do I need previous AI or machine-learning experience?
No deep AI or ML background is required. The mentorship starts by connecting your software engineering base to LLM applications, RAG, agents, evaluation and deployment. It is not an ML research program.
How much Python do I need before joining?
You should be willing to write practical Python and learn quickly. You do not need to be a Python specialist before joining, but you should already understand programming, APIs, data structures and software-development workflow.
Is this a beginner course?
No. The mentorship assumes professional software experience and uses that experience as the foundation. It is not built for people with no programming or software engineering background.
Is this suitable for someone with fewer than 10 years of experience?
The primary audience is 10+ year professionals. A strong engineer with fewer years may still be a fit if they have substantial production experience, maturity and the ability to keep up with architecture-first project work.
Will I build real projects?
Yes. The program is organised around building systems, not only watching content. Projects include LLM applications, RAG systems, agentic workflows, evaluation pipelines and a capstone system.
Will I deploy an AI system?
Deployment is part of the path. The capstone is intended to help you build, evaluate, deploy, monitor and explain a production-ready AI system.
Is the program live or recorded?
The current page identifies this as a live weekend cohort. The value comes from structured live learning, reviews, feedback and project work.
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 weekend schedule and career positioning are designed for experienced Indian working professionals who are balancing delivery responsibilities with a serious transition into AI Engineering.
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 and appraisals?
The program is designed to help you build systems, architecture explanations and a credible career narrative. That can support interviews, internal AI initiatives and appraisal conversations, but it does not guarantee an outcome.
Do you provide placement or guarantee a job?
No. This is not a placement-guarantee program. The mentorship helps you build the technical capability, deployed work and professional narrative required for AI Engineering 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 courses?
Self-paced content can explain concepts. This mentorship is organised around architecture, implementation, reviews, feedback, deployment and career narrative for experienced professionals.
I have already purchased AI courses. Why should I consider this?
If those courses gave you disconnected concepts but not a system you can design, deploy and explain, this program gives you a structured transition path and project-review loop.
What happens after the mentorship ends?
By the end of the mentorship, you should have a much stronger AI Engineering 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 or enquiry flow 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.

AI Engineer-Specific FAQs

How the technical AI Engineering content is handled.

Will the program cover LLM Engineering?
Yes. LLM Engineering is a dedicated curriculum module covering model APIs, model selection, context engineering, structured generation, tool calling, streaming, fallback, routing, cost, latency and reliability.
Will I build RAG systems?
Yes. The curriculum includes document ingestion, chunking, embeddings, vector databases, retrieval strategies, reranking, citations, source attribution, retrieval evaluation and enterprise RAG architecture patterns.
Which agentic AI frameworks will be covered?
The page represents OpenAI Agents SDK, LangGraph, CrewAI, Google ADK and LangChain where appropriate. They are treated as implementation options, not as the centre of the program.
Will we use OpenAI Agents SDK, LangGraph, CrewAI and Google ADK?
Yes, these are represented in the Agentic AI Engineering module. The goal is to understand architecture patterns, framework selection and trade-offs rather than memorising every API equally.
Will LangChain be included?
LangChain is included where appropriate. The emphasis remains on choosing the right abstraction for the problem and avoiding unnecessary framework complexity.
Does the program cover MLOps and LLMOps?
Yes. The production-readiness module includes LLMOps workflows, relevant MLOps foundations, versioning, monitoring, tracing, observability, CI/CD, deployment and production incident thinking.
Will we cover evaluation, observability and guardrails?
Yes. Evaluation, safety, guardrails, retrieval evaluation, response-quality evaluation, hallucination analysis, tracing, monitoring and observability are explicit parts of the curriculum.
Will I learn deployment and cloud integration?
Yes. Deployment, Docker, service layers, CI/CD, cloud deployment, secrets management, security, scalability and integration with existing APIs and databases are included.
Is this a data-science or machine-learning research program?
No. It is an AI Engineering mentorship focused on production applications, architecture, integration, deployment, evaluation and business impact. It is not a research-heavy ML curriculum.
Will I be able to integrate AI into my existing enterprise stack?
That is the point of the architecture and integration module: connecting AI systems to existing APIs, databases, workflows, authentication, authorisation, cloud architecture and production readiness practices.

Check Whether This Path Fits You

Suitable if

  • - You have substantial professional software engineering experience
  • - You have worked with production applications or enterprise systems
  • - You are comfortable with programming and software-development concepts
  • - You want to build and deploy AI systems
  • - You can commit consistent time each week
  • - You are willing to receive feedback and complete project work
  • - You want a serious career transition rather than a quick certificate

Not suitable if

  • - You are looking only for prompt tricks or AI-tool demonstrations
  • - You have no programming or software-engineering foundation
  • - You want a data-science or ML-research curriculum
  • - You expect guaranteed placement without doing the project work
  • - You want a passive, self-paced video course
  • - You cannot commit time to implementation
  • - You are only collecting certificates

Do Not Let Your Next Appraisal Say Only:
"Maintained Existing Systems."

Build the skills, system and career narrative that position you as the engineer who can design, deploy and lead AI-powered systems.

Rewrite My AI Career Story

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