Designing Production AI Systems

AI Systems Engineering for Software Engineers

A professional training program by Ranjan Kumar ranjankumar.in

The missing engineering layer between LLM APIs and production AI systems.

The Problem No AI Course Is Solving

Every software engineer is now being asked the same question by management:

"How are we using AI in our product?"

Most engineers were never trained to answer it. Not because they lack AI skills. Because they were never taught how AI systems actually work.

Most AI education teaches tools. Software engineers need to learn systems.

Today many engineers can call an LLM API. Very few engineers know how to design the system around it — the retrieval layer, the evaluation framework, the observability pipeline, the architectural patterns that determine whether the system holds up in production.

A team deployed an internal RAG assistant for support engineers.

The demo worked perfectly.

Within two weeks, support engineers stopped using it.

The answers were confident — and wrong.

The problem wasn't the model. It was the retrieval system. Nobody was measuring retrieval quality. The fix required redesigning the system architecture, not adjusting the prompt.

What This Program Teaches

This is not a prompt engineering course. This is not a machine learning course.

This is AI Systems Engineering — the discipline of designing, building, and operating AI-powered software in production.

flowchart LR
    subgraph Era1["❌ AI Tools Era"]
        P[Prompt] --> R[Response]
    end

    subgraph Era2["✅ AI Systems Era"]
        U[User] --> G[Gateway]
        G --> RA[RAG]
        RA --> T[Tools]
        T --> E[Eval]
        E --> O[Observability]
    end

    Era1 -.->|"The shift this program teaches"| Era2

    style Era1 fill:#f8f8f8,stroke:#ccc
    style Era2 fill:#f0f7ff,stroke:#4A90E2
    style P fill:#95A5A6,color:#fff
    style R fill:#95A5A6,color:#fff
    style U fill:#4A90E2,color:#fff
    style G fill:#6BCF7F,color:#333
    style RA fill:#98D8C8,color:#333
    style T fill:#FFD93D,color:#333
    style E fill:#FFA07A,color:#333
    style O fill:#9B59B6,color:#fff

That system is where engineering lives. That's what this program teaches.

Who This Program Is For

This program is designed for software engineers building AI-powered products.

Ideal participants:

• Backend engineers integrating AI features into products
• Staff engineers and tech leads responsible for AI architecture decisions
• Engineering managers guiding AI initiatives
• Startup engineers building AI-first products

Recommended background:

• 3–15 years of software engineering experience
• Experience with APIs or backend systems
• Basic exposure to LLM tools (no ML background required)

The Core Framework: The 7 GenAI Architectures

At the center of this program is a decision framework developed from years of building and reviewing production AI systems. Almost every modern GenAI application falls into one of seven architectural patterns. Once you see them, you start recognizing them everywhere.

flowchart LR
    L0[Level 0\nDeterministic] --> L1[Level 1\nPrompt App]
    L1 --> L2[Level 2\nRAG]
    L2 --> L3[Level 3\nWorkflow]
    L3 --> L4[Level 4\nTool LLM]
    L4 --> L5[Level 5\nReasoning]
    L5 --> L6[Level 6\nAgent]
    L6 --> L7[Level 7\nMulti-Agent]

    style L0 fill:#95A5A6,color:#fff
    style L1 fill:#6BCF7F,color:#333
    style L2 fill:#98D8C8,color:#333
    style L3 fill:#4A90E2,color:#fff
    style L4 fill:#FFD93D,color:#333
    style L5 fill:#FFA07A,color:#333
    style L6 fill:#E74C3C,color:#fff
    style L7 fill:#9B59B6,color:#fff

The rule experienced AI engineers learn the hard way:

Start at Level 0 and move right only when the current level fails. Every step up the spectrum should be earned by a problem the previous level couldn't solve.

Each level adds capability — and cost and complexity in direct proportion. Understanding this spectrum gives engineers a decision criterion before they build.

Program Structure

The curriculum progresses from AI system foundations → application architectures → production engineering.

Week-by-Week Curriculum

Week 1 — AI Systems Foundations

How modern AI systems are actually structured.

• AI vs ML vs GenAI — clarifying the landscape
• AI systems vs AI models — the critical distinction
• The architecture layers of AI applications
• The 7 GenAI Architectures decision framework

flowchart LR
    A[User Request] --> B[LLM Gateway]
    B --> C[RAG Pipeline]
    C --> D[Vector Database]
    D --> E[Tool Execution]
    E --> F[Evaluation Layer]
    F --> G[Observability]
    G --> H[Response]

    style B fill:#4A90E2,color:#fff
    style C fill:#98D8C8,color:#333
    style D fill:#FFD93D,color:#333
    style E fill:#6BCF7F,color:#333
    style F fill:#FFA07A,color:#333
    style G fill:#9B59B6,color:#fff

Lab: Architecture analysis of real-world AI products — identifying which level each system operates at and why.

Week 2 — RAG Architecture and Retrieval Engineering

Why most RAG systems fail in production — and how to build ones that don't.

• Chunking strategies and their failure modes
• Embedding model selection
• Vector database design
• Hybrid search: BM25 + dense retrieval
• Reranking strategies and cross-encoders
• Retrieval quality evaluation

flowchart LR
    Q[User Query] --> E[Embedding Model]
    E --> V[(Vector Database)]
    V --> R[Reranker]
    R --> C[Retrieved Context]
    C --> L[LLM]
    L --> A[Answer]

    style E fill:#FFD93D,color:#333
    style V fill:#98D8C8,color:#333
    style R fill:#FFA07A,color:#333
    style L fill:#4A90E2,color:#fff

Common failures explored: bad chunking strategies, irrelevant retrieval, hallucinated citations, context overflow, stale indexes.

Lab: Build and evaluate a RAG system. Measure retrieval quality before and after applying reranking.

Week 3 — Tool-Using LLM Systems

How to design AI systems that interact with the real world reliably.

• Function calling architecture
• Tool schema design — why it's harder than it looks
• SQL assistant patterns
• API orchestration
• Error handling and tool retries
• Rate limiting and consequence modeling

flowchart LR
    A[User Request] --> B[LLM]
    B --> C{Tool Decision}
    C --> D[Database Query]
    C --> E[External API]
    C --> F[Code Execution]
    D --> G[Result]
    E --> G
    F --> G
    G --> H[LLM Response]

    style B fill:#4A90E2,color:#fff
    style C fill:#FFD93D,color:#333

Lab: Build a tool-using LLM with multiple tool types. Design error recovery and implement rate limiting.

Week 4 — Autonomous Agents

When agents are actually necessary — and how to build ones that don't fail catastrophically.

• Reasoning loop architecture
• Agent memory: scratchpad, vector memory, task history
• Planning patterns
• Consequence modeling before execution
• Agent security: prompt injection, credential scoping, the agent DMZ pattern
• Failure modes: infinite loops, context overflow, cost explosion

flowchart TD
    A[Goal] --> B[Reason]
    B --> C[Select Tool]
    C --> D[Execute Tool]
    D --> E[Observe Result]
    E --> F{Goal Achieved?}
    F -->|No| B
    F -->|Yes| G[Final Output]

    style B fill:#4A90E2,color:#fff
    style C fill:#FFD93D,color:#333
    style D fill:#6BCF7F,color:#333
    style E fill:#98D8C8,color:#333
    style F fill:#E74C3C,color:#fff

Lab: Build an autonomous agent with hard constraints, consequence modeling, and an audit trail.

Week 5 — Production AI Engineering

How to operate AI systems at scale — evaluation, observability, cost, and reliability.

• Evaluation frameworks for LLM outputs
• RAG quality measurement in production
• LLM observability and distributed tracing
• Context management at scale
• Cost architecture and latency budgets
• Debugging AI systems: failure mode taxonomy

Production observability architecture:

flowchart LR
    A[User Request] --> B[LLM Gateway]
    B --> C[Trace ID]
    C --> D[RAG Span]
    D --> E[Tool Span]
    E --> F[Eval Span]
    F --> G[Observability Platform]
    G --> H[Dashboards + Alerts]

    style B fill:#4A90E2,color:#fff
    style G fill:#9B59B6,color:#fff

Lab: Instrument an AI system end-to-end. Identify a retrieval failure from trace data.

Week 6 — Capstone Project

Design a production AI system from first principles.

Participants design a complete production AI system architecture for a realistic product scenario.

Deliverables:

• Architecture design with justification for each level chosen
• Evaluation strategy with defined quality metrics
• Observability plan with tracing and alerting design
• Cost model with latency budgets per component

Example capstone scenarios:

• Enterprise internal knowledge assistant
• AI-powered customer support system
• Developer productivity copilot
• AI analytics assistant

What You Will Be Able to Do After This Program

Engineers who complete this program will be able to design AI systems the way they design distributed systems — with clear architectures, failure modes, and observability.

Specifically, you will be able to:

• Choose the right architecture for any AI product requirement — before writing the first line of code
• Design RAG pipelines that hold up under real query load and don't hallucinate at scale
• Build tool-using LLM systems with proper schema design, error handling, and rate limiting
• Implement autonomous agents with consequence modeling, memory architecture, and hard safety constraints
• Instrument AI systems with distributed tracing and observability that spans the full system
• Debug AI failures by identifying the system layer where the failure originates — not just adjusting the prompt

What This Program Does NOT Teach

This is a deliberate scope decision, not a gap.

Most AI courses cover neural networks, gradient descent, model training, and fine-tuning. That makes sense for ML researchers.

Software engineers building AI-powered products have a different job. They don't need to train models. They need to build reliable systems around them.

This program focuses entirely on that job.

Why This Program Is Different

Value for Engineering Leaders

A mis-designed RAG pipeline doesn't just return wrong answers — it generates support load, erodes user trust, and requires a rebuild under deadline pressure. A premature autonomous agent creates security exposure, uncontrolled API costs, and debugging nightmares.

This program compresses years of production AI systems experience into six weeks. Engineers leave with a framework they apply to every AI project they touch.

What this delivers for your team:

• Avoid expensive architecture mistakes before they're baked into the codebase
• Reduce AI project failures caused by over-engineering or mismatched architecture
• Shorten experimentation cycles with a shared decision framework across the team
• Reduce hallucination risk by understanding where in the system it originates
• Establish a shared engineering vocabulary for AI system design

About the Instructor

Ranjan Kumar

AI Systems Engineering Educator ranjankumar.in

Ranjan Kumar is an AI systems engineering educator focused on the layer between models and production systems — RAG architectures, LLM pipelines, agentic systems, AI observability, and AI infrastructure.

His work analyzes real failure modes in production AI systems and provides architectural frameworks engineers can apply before building.

The 7 GenAI Architectures framework taught in this program helps teams choose the correct AI architecture before committing to implementation.

Enrollment Options

This training is offered in two formats.

Private Team Workshop

For engineering teams at companies

Delivered directly to your engineering team. Format and depth adjusted to your team's current experience level and the specific AI systems you are building.

• One-day intensive (architectures + decision framework)
• Two-day bootcamp (RAG + tool systems + implementation)
• Full six-week program (complete production AI engineering)

Public Cohort

For individual engineers

Small cohort format. Live instruction, hands-on labs, peer architecture reviews, capstone project.

Get Started

Engineering leaders: Request a team workshop at ranjankumar.in/contact

Individual engineers: Join the waitlist at ranjankumar.in/contact

Not sure yet? Read the 7 GenAI Architectures framework and other articles at ranjankumar.in/blog

The Hard Part Isn't the Model

AI tools are improving every month. AI models are improving every quarter.

But the need for engineers who understand AI systems will only grow.

Because the hard part isn't calling the model. It's designing the system around it.

This program exists to close that gap.

AI Systems Engineering for Software Engineers — Ranjan Kumar ranjankumar.in · ranjankumar.in/contact