𝐀𝐈 𝐄𝐧𝐭𝐡𝐮𝐬𝐢𝐚𝐬𝐭 | 𝐅𝐮𝐥𝐥 𝐒𝐭𝐚𝐜𝐤 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 | 𝐆𝐫𝐨𝐰𝐭𝐡 𝐌𝐚𝐫𝐤𝐞𝐭𝐞𝐫 | 𝐒𝐡𝐚𝐫𝐢𝐧𝐠 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐨𝐧 𝐀𝐈 | 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 𝐖𝐫𝐢𝐭𝐞𝐫

As a software developer with 3 years of experience, I am passionate about crafting high-quality software that solves complex problems and delivers value to end-users. With a strong background in Flutter,ASP .NET and Database technologies, I thrive on collaborating with cross-functional teams to design and implement solutions that meet business requirements.

My expertise spans the entire software development lifecycle, from requirements gathering and architecture design to coding, testing, deployment, and maintenance. I am comfortable working with a wide range of programming languages, frameworks, and tools, and am always eager to learn new technologies to expand my skill set.

If you are looking for a software developer who can help you achieve your business goals, please don't hesitate to get in touch. I am excited to explore new opportunities and to leverage my skills and experience to deliver value to your organization.

Aakash K.

Disclaimer

All views are strictly my own. I do not speak on behalf of my employer. My content focuses on publicly available knowledge, frameworks, and industry trends that help advance the developer and AI community.

I am an AI Architect and Enterprise Architecture leader with 16+ years of experience, focused on Generative AI, Agentic AI, and enterprise-scale AI platforms, with a strong foundation in classical machine learning and data-driven systems.

In my current role at Wells Fargo, I work within Enterprise Architecture, designing scalable, secure AI and data platforms across the bank’s global footprint. My work spans classical ML, Generative AI, and Agentic systems, applying advanced LLMs, multi-agent orchestration, and AI governance frameworks alongside traditional ML models to real-world financial services use cases in a regulated, production environment.

I specialize in architecting end-to-end AI systems—from feature engineering, model training, and evaluation to RAG pipelines, agentic decision layers, tool-using agents, and autonomous workflows. My architectures integrate classical ML techniques (regression, classification, clustering, time-series forecasting, anomaly detection) with modern LLM-based systems, enabling robust, explainable, and scalable AI solutions.

My work includes cloud and data engineering on AWS, Azure , GCP, designing resilient, high-performance architectures using modern data stacks, while ensuring security, compliance, governance, and operational excellence across on-prem, hybrid, and multi-cloud environments.

Beyond delivery, I provide technical and architectural leadership across product, engineering, and business teams—aligning enterprise AI strategy with hands-on execution and translating emerging AI capabilities into measurable business outcomes.


Core Focus Areas:

Generative AI & Agentic Systems
LLMs, Multi-Agent Architectures, Agent Orchestration, RAG, Tool Calling, Autonomous Workflows, AI Reasoning & Planning

Classical Machine Learning
Regression, Classification, Clustering, Time-Series Forecasting, Anomaly Detection, Feature Engineering, Model Evaluation

Enterprise AI Platforms & MLOps
Production ML & LLM Systems, Model Governance, MLflow, CI/CD for AI, Monitoring & Observability, Responsible AI

Architecture & Leadership
Enterprise Architecture, AI Strategy, Technical Roadmapping, Cross-functional Collaboration, Team Mentorship

Brij Pandey

Update  - This certification is currently available only through Anthropic partners. It was free for the first 5,000 users, but it is now priced at $99.

Anthropic just announced the "Claude Certified Architect" program.
And you can start today.

In 16 years of my professional career, I haven't done a single certification.
Not one.

Not AWS. Not Azure. Not Google Cloud. Not PMP. Not Scrum. Not any of the alphabet soup.

I learned by building. By breaking things. By shipping.
But I'm about to break that streak.

I'm going for my first-ever certification:
Claude Certified Architect — Foundations
Here's why this matters — especially if you're a developer, engineer, or any professional who feels like the AI wave is moving too fast.

Claude Code launched a few weeks ago.
And it feels like a paradigm shift.
Not an incremental upgrade. Not another chatbot wrapper.
A fundamentally different way of building software.
Agentic architecture. Tool orchestration. MCP integration. Context management at a systems level.

If those words sound intimidating — that's exactly why this certification exists.
It covers everything from agentic orchestration to prompt engineering to Claude Code workflows.

Not surface-level content.

And here's what got me:
It costs nothing.
Free. Zero. $0.

So if you've been feeling left behind... If you've been watching others ship AI agents while you're still figuring out where to start... If you've been telling yourself "I'll learn this next quarter"...

This is your sign.
Stop scrolling. Start building.

First certification in 16 years. Let's see how this goes.
Links in the comments 👇

Brij kishore Pandey

Most people treat CLAUDE. md like a “prompt file”.
That’s the mistake.

If you want Claude Code to feel like a senior engineer living inside your repo, you need to structure the project so the model always has:

the why (what this system is meant to do)
the map (where things live)
the rules (what’s allowed + what’s not)
the repeatable workflows (how work gets done here)

That’s what I call:
The Anatomy of a Claude Code Project

Here’s the structure (and why it works):
1) CLAUDE. md = repo memory (keep it short)
This is the “north star” file.
 Not a dump of everything you know — just:
purpose (WHY)
repo map (WHAT)
working rules + commands (HOW)
If it gets long, Claude starts missing the important parts.

2) .claude/skills/ = reusable “expert modes”
Instead of rewriting the same instructions every time:
code review checklist
refactor playbook
release procedure
debugging flow
Turn them into skills.
 This is how you get consistency across sessions and across teammates.

3) .claude/hooks/ = guardrails you don’t trust memory for
Models forget.
 Hooks don’t.
Use hooks for things that must be deterministic:
run formatter after edits
run tests on core changes
block unsafe directories (migrations, auth, billing)

4) docs/ = progressive disclosure (don’t bloat prompts)
Claude doesn’t need everything in the main context.
 It needs to know where the truth lives:
architecture overview
ADRs (decisions)
runbooks for operations

5) local CLAUDE. md inside risky modules
Put small CLAUDE. md files near the sharp edges:
src/auth/CLAUDE. md
src/persistence/CLAUDE. md
infra/CLAUDE. md
Now Claude picks up the “gotchas” exactly when it works in that folder.


Prompting is temporary. Structure is permanent.
When your repo is organized this way, Claude stops being a chatbot…
…and starts acting like a project-native engineer.

Let me know, I can share a starter template for:
CLAUDE. md
Skills files
Hook ideas in my next post!

AI Architect | AI Engineer | Generative AI | Agentic AI

I spent the last few weeks living inside Claude Code.

Not just using it. Building with it. Breaking it. Figuring out what actually makes it work well vs. what wastes your time.

The biggest lesson?

Claude Code isn't just a terminal chatbot. It's a 4-layer system:

→ CLAUDE. md — your project's persistent memory
→ Skills — knowledge packs Claude auto-invokes when relevant
→ Hooks — deterministic safety gates (100% enforced, not "suggestions")
→ Agents — subagents with their own context windows

Most engineers install it, type a prompt, do a few things here and there and wonder why the output is mid.

The difference between average and exceptional results comes down to setup:

→ Run /init on day one to generate your CLAUDE. md
→ Structure your .claude/ directory with skills, hooks, and permissions
→ Write descriptions that actually trigger the right skills at the right time
→ Use the memory hierarchy (global → project → subfolder) to scope context
→ Set up hooks for safety rules — because CLAUDE. md rules are ~70% followed, hooks are 100%

I put all of this into a single-page A4 cheatsheet.

14 sections. 3 columns. Everything from first install to the 4-layer architecture.

Not a command dump. A workflow guide:

✦ How to get started (install → /init → first session)
✦ How to write an effective CLAUDE. md (WHAT / WHY / HOW framework)
✦ Exact file structure for a fully configured project
✦ How to add and write skills that actually trigger correctly
✦ Setting up hooks and permissions in settings. json
✦ Daily workflow pattern (Plan Mode → Auto-Accept → commit loop)

If you're using Claude Code without setting up CLAUDE. md and skills — you're leaving 80% of its power on the table.

If you want to actually understand LLMs and Generative AI (not just copy prompts), this Stanford lecture series is still one of the cleanest, most structured, step-by-step paths I’ve found.

Stanford LLM Lecture Series (watch in order):

Transformer Fundamentals — https://lnkd.in/gaaRDexT
Transformer Models + Practical Tricks — https://lnkd.in/gy4FUwNY
Transformers → Large Language Models — https://lnkd.in/gsPiCrEU
How LLMs Are Trained — https://lnkd.in/gvHJvgqP
Tuning & Adaptation (fine-tuning, etc.) — https://lnkd.in/g6kgtPKR
Reasoning in LLMs — https://lnkd.in/gAACSUG6
Agentic LLMs (tools, planning, workflows) — https://lnkd.in/gVm6js9z
Evaluation: what “good” really means — https://lnkd.in/gJhbFQ4s
Recap + What’s trending now — https://lnkd.in/g5JMNTsf

To make this path more practical and well-rounded, I’d pair the Stanford theory with these free, hands-on resources:

• Microsoft – Generative AI for Beginners (FREE):
https://lnkd.in/dTPBrQb4
• NVIDIA Developer Program (FREE access to tools, SDKs, learning):
https://lnkd.in/ejMfdtuS
• Google Cloud – ML & AI Training (FREE tracks):
https://lnkd.in/eUP8zNik

How I’d approach it:

Treat this like a mini-bootcamp. One lecture at a time. Take notes. Pause often. Implement small experiments after each session.

Theory + hands-on labs + real platforms is what actually builds intuition.

If you’re learning LLMs heading into 2026, this combination gives you a rock-solid foundation, from fundamentals to agents to real deployment thinking.

Most people mix these up:

RAG, AI Agents, MCP, A2A aren’t competitors.

They’re different layers of the same AI system.
Here’s the simplest mental model (matches the infographic):

1) RAG = “Better answers”
RAG is about retrieval + grounded generation.
Flow:
User question → rewrite/embed → retrieve top-k chunks → rerank/filter → compose prompt (question + context) → LLM answers + citations
Use RAG when your problem is:
“Answer using our docs”
“Reduce hallucinations”
“Show sources”
RAG ends when the model has enough context to respond.

2) AI Agents = “Do the work”
Agents are about planning + acting in a loop.
Loop: Plan → Observe → Act → Reflect (with memory read/write)
Use agents when the problem requires:
multi-step decisions
tool usage + verification
closed-loop execution in real systems (tickets, emails, data tools)
Agents start where retrieval stops: actions + outcomes.

3) MCP = “Standardized tool access”
MCP is the missing plumbing: a clean protocol layer that lets hosts (IDE, desktop assistant, internal playground) connect to tool servers.
Think: “One standard way for LLMs to use tools + resources.”
Use MCP when you want:
consistent tool interfaces across apps
governed access to CRM / SQL / file drives / internal services
fewer one-off tool integrations
MCP is not “an agent.” It’s the tool connectivity layer.

4) A2A = “Agents coordinating with agents”
A2A is for multi-agent ecosystems: discovery, delegation, routing, permissions, handoffs, status/events, and artifacts.
Use A2A when you have:
many specialized agents (support, SRE, finance, analytics)
remote agents (vendors/partners)
real coordination needs (handoffs, escalations, audit trails)
A2A is not tool calling. It’s agent-to-agent orchestration at scale.
The practical takeaway

If you’re building an enterprise AI system, the stack often looks like:
RAG (grounding) + Agents (execution) + MCP (tool layer) + A2A (coordination layer)
Different problems. Different lanes. One architecture.
If you want, I can also share:
a “when to use what” decision chart
a reference architecture for an enterprise agent platform using all 4 layers

Stop building RAG like it's 2023.
We all know the basic recipe: Chunk → Embed → Retrieve → Generate.
It works great… until it doesn't.

The moment you go from weekend prototype to enterprise production, that simple pipeline falls apart.

I mapped out what a truly Robust RAG System actually looks like under the hood. Here's what most teams are missing:
━━━━━━━━━━━━━━━━━━━━━━━
𝟭. 𝗤𝘂𝗲𝗿𝘆 𝗖𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 ≠ 𝗝𝘂𝘀𝘁 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝗲𝗮𝗿𝗰𝗵
Real queries need multiple backends: ↳ Graph DBs for relationship-heavy questions ↳ SQL for structured/numerical data ↳ Vector search for semantic meaning
One retrieval path can't handle all three.
𝟮. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗥𝗼𝘂𝘁𝗶𝗻𝗴
Before you even retrieve, you need to decide: ↳ Semantic route or logical route? ↳ Single-hop or multi-hop? ↳ Which data source to hit first?
This one decision layer saves you from 80% of bad retrievals.
𝟯. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴
If you're still doing naive chunking, you're leaving accuracy on the table. ↳ RAPTOR → recursive abstractive processing for hierarchical understanding ↳ ColBERT → token-level semantic matching for precision retrieval ↳ Multi-representation indexing → different views of the same data
𝟰. 𝗧𝗵𝗲 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗟𝗼𝗼𝗽 (𝗡𝗼𝗻-𝗡𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲)
You can't improve what you can't measure. ↳ Ragas for end-to-end RAG evaluation ↳ DeepEval for component-level testing ↳ Continuous monitoring, not one-time benchmarks
━━━━━━━━━━━━━━━━━━━━━━━
Here's the hard truth:
RAG isn't a feature anymore. It's a full engineering system.
And the teams treating it like a quick integration are the ones wondering why their AI "hallucinates."
The gap between a demo and production RAG? It's these 4 layers.

AI isn't magic. 
It’s a series of building blocks stacked on top of decades of research. 

I love this visualization because it perfectly captures the hierarchy of artificial intelligence. 
We often get caught up in the hype of the top layers, but none of it would exist without the foundation below it.
You can't have Deep Learning without understanding Neural Networks. 
You can't have Generative AI without the breakthroughs in Deep Learning.
And now, we are seeing the rise of Agentic AI sitting at the very top—moving beyond just generating content to actually planning, using tools, and executing tasks autonomously.

To build the future, you have to understand the foundations.
The stack keeps growing. Are you ready for the Agentic era?

Every week, I share one FREE AI resource that's actually worth your time.

This week's pick: Microsoft's "AI Agents for Beginners"

It's a full open-source course on GitHub.
10 lessons. Concept to code. Zero cost.

What you'll learn:

→ What AI agents are (and aren't)
→ Choosing the right agent framework
→ Designing agents that actually work
→ Agentic RAG
→ Tool Use Design Pattern
→ Deploying agents to production

Everything comes with code samples you can run yourself.

I've gone through dozens of AI agent resources.

This one punches way above its price tag (which is $0).

🔗 https://lnkd.in/eZeXXee5

Save this post. You'll thank yourself later.

A lot of people want to learn Claude Code, MCP, and agentic AI, but most end up jumping between random tutorials, scattered posts, and incomplete videos.

So I wanted to simplify it.

This one-page list brings together 15 free courses and learning resources that can help you understand:

 Claude fundamentals
 Claude Code
 Claude API
 Model Context Protocol (MCP)
 Agent skills
 Prompt engineering
 Claude on AWS Bedrock
 Claude on Google Vertex AI

What I like about this list is that it covers both sides of the journey.

You can start with beginner-friendly courses like Claude 101 and AI Fluency, then move into more practical topics like Claude API, MCP servers, advanced MCP patterns, and agent skills.

If you are serious about building AI applications in 2026, I think these are worth bookmarking.

APIs are becoming versioned, AI-managed code assets.
And the new Postman release is pushing this shift forward.

For years, the API workflow was fragmented:
→ Code lives in Git 
→ API specs live somewhere else 
→ Tests run somewhere else 
→ Mocks are managed separately

The result? Broken environments. Outdated docs. "Works on my machine" chaos. Constant context switching between tools.

Postman is changing that model entirely.
Their new version introduces something developers have been asking for:
Git-native APIs. Workflows start locally. AI handles the coordination. 

That’s the shift.
Here's what caught my attention:

𝟭. 𝗔𝗣𝗜𝘀 𝗻𝗼𝘄 𝗹𝗶𝘃𝗲 𝗶𝗻𝘀𝗶𝗱𝗲 𝗚𝗶𝘁
API specs, collections, mocks, tests, environments — all live directly in your Git repository.
That means branching, pull requests, version history, and code reviews now apply to APIs exactly the same way they apply to code.
This alone is a massive shift for API lifecycle management.

𝟮. 𝗟𝗼𝗰𝗮𝗹-𝗳𝗶𝗿𝘀𝘁 𝗔𝗣𝗜 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁
Developers can now work directly from the local filesystem.
Design → test → mock → validate APIs locally before anything touches CI.
This eliminates the classic "it works locally but fails in CI" problem.
Because the same workflows run both locally and in CI/CD pipelines.

𝟯. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗔𝗣𝗜 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲
This is the most interesting piece — Agent Mode.
Instead of just generating code, AI can now coordinate multi-step API changes.
You change an endpoint. AI automatically updates:
→ The spec → Request collections → Tests → Mocks

All while keeping everything versioned.
That's AI operating across the entire API lifecycle, not just code generation.

𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀
The industry is shifting toward AI-assisted development, Git-centric workflows, and automated testing.

Postman is aligning API workflows with how modern software engineering actually works.
APIs are no longer just endpoints.
They are living assets that evolve with your codebase.
If you build APIs, it's worth exploring how this changes the workflow.
Thanks to Postman for sponsoring this post.

Explore the New Postman : https://fandf.co/4rj9t5n

I've been silent for a reason.
I was busy building my own AI agent with OpenClaw.

Yes — I configured it on Windows (inside WSL). And I just ordered a Mac Mini that arrives tomorrow.
This is just the beginning.
OpenClaw is an open-source framework that lets you build autonomous AI agents running locally on your machine.
It connects to LLMs like Claude, GPT, and DeepSeek — and your agents can control email, calendar, files, APIs, and messaging platforms.

Here's my learning roadmap to master it in ~5 weeks:

𝗣𝗵𝗮𝘀𝗲 𝟭 — 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 (𝗗𝗮𝘆𝘀 𝟭-𝟯) 
→ Gateway → LLM → Tools pipeline → Serial Lane Queue execution model → Skills, ClawHub, Heartbeats, SOUL. md
𝗣𝗵𝗮𝘀𝗲 𝟮 — 𝗦𝗲𝘁𝘂𝗽 & 𝗙𝗶𝗿𝘀𝘁 𝗥𝘂𝗻 (𝗗𝗮𝘆𝘀 𝟰-𝟲) 
→ Install via npm → Launch control UI locally → Connect Telegram or WhatsApp
𝗣𝗵𝗮𝘀𝗲 𝟯 — 𝗗𝗮𝗶𝗹𝘆 𝗗𝗿𝗶𝘃𝗲𝗿 (𝗗𝗮𝘆𝘀 𝟳-𝟭𝟰) 
→ Connect email & calendar → Install community skills → Customize agent personality (SOUL. md) → Automate daily summaries
𝗣𝗵𝗮𝘀𝗲 𝟰 — 𝗦𝗸𝗶𝗹𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 (𝗗𝗮𝘆𝘀 𝟭𝟱-𝟮𝟱) 
→ Build custom skills with SKILL. md → Write Python / TypeScript scripts → Docker sandbox for safe execution
𝗣𝗵𝗮𝘀𝗲 𝟱 — 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 (𝗗𝗮𝘆𝘀 𝟮𝟲-𝟯𝟱) → Multi-agent orchestration → Voice interaction → Browser automation → Event-driven workflows
𝗣𝗵𝗮𝘀𝗲 𝟲 — 𝗖𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗲 & 𝗦𝗰𝗮𝗹𝗲 (𝗗𝗮𝘆 𝟯𝟲+) → Publish skills to ClawHub → Contribute to open source → Model routing strategies

Don't skip the security essentials: 
⚡ Docker sandboxing 
⚡ Restrict allowed sources 
⚡ Protect against prompt injection

My plan?
Start with personal automation first: 
→ Daily news summaries 
→ Email triage 
→ Calendar management 
→ Content research

Then scale to workflows.
"Start small. Solve one real problem. Let your agent evolve."

The Mac Mini arrives tomorrow.
I'll document everything.
Stay tuned.
Here is my hardware for windows - 

NVIDIA RTX PRO 5000 Blackwell — 1,824 AI TOPS, 24GB GDDR7, 896 GB/s memory bandwidth.

Logan Lawler
#DellProMax #DellTech #NVIDIA #DellProPrecision

I've structured this project to be highly organized and scalable. 
Here's a quick breakdown:

.claude/: The heart of the AI's "operating system." This directory contains everything specific to the Claude model, including hooks for automation and a dedicated skills/ folder for reusable workflows.

config/: Centralized management for all configurations. Separating environment variables from model parameters ensures security and flexibility.

src/: The main logic hub.

agents/: Where the custom agent implementations live, directly referencing the skills defined in the .claude/ folder.

core/: The "brain" of the operation. This handles memory (state management), reasoning (prompt chain logic), and planning (task decomposition).

utils/: A home for helper functions, including externalized prompt templates to make them easy to update and manage.

docs/: Crucial for collaboration and long-term maintainability. This section is dedicated to architectural context and Architecture Decision Records (ADRs).

tests/: No project is complete without rigorous evaluation and unit tests to ensure everything is working as expected.

I believe that a clean and well-thought-out structure is essential for building robust and maintainable AI applications. 

This layout not only makes it easier for me to develop and iterate but also provides a clear roadmap for anyone else who might contribute in the future.

taplio

The Battle

Aakash K.

Brij Pandey

Followers

Posts

Engagements

Likes

Performance ScoreBrij Pandey's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Aakash K.

Brij Pandey

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Brij Pandey's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreBrij Pandey's LinkedIn Performance