u/NeedleworkerNo6683

Honest truth nobody says:

Most "AI developers" you'll hire this year will cost you more than they save you.

Not because they're bad people.

Because they learned AI from Twitter threads and YouTube tutorials — and you won't find out until your product is already in production and breaking.

I know this because I've been called in to fix what they built.

Here's what I've actually cleaned up:

→ A RAG system where every answer was confidently wrong — chunks were so small the AI had zero context to work with

→ An AI agent that restarted from scratch every time an API timed out — no checkpointing, no state recovery, just silent failure

→ A FastAPI backend doing synchronous LLM calls — 10 concurrent users and the whole thing queued up and died

→ A chatbot leaking conversation history across different users — a data privacy disaster waiting to happen

→ An AWS deployment with hardcoded API keys in the codebase — pushed to a public repo

None of these were built by lazy people. They were built by developers who didn't know what they didn't know.

What I do differently:

I treat AI systems like production infrastructure — not research projects.

That means:

On RAG:

Semantic chunking over fixed-size. Hybrid search. Reranking. Metadata filtering. Not just "embed and retrieve."

On Agents:

Checkpoint every tool call to DB. Separate working memory from conversation history. Budget your context window. Plan for failure from day one.

On APIs:

Async from the start. Retry logic built in. Fallback models configured. Timeout budgets set. Never synchronous LLM calls on the main thread.

On Deployment:

Secrets management. Proper logging. Health checks. Graceful degradation. Not just "it works on my machine."

This is who I am:

3 years building backend and AI systems in production. Not side projects. Not tutorials. Production systems with real users, real data, real consequences when things break.

Stack I work in daily:

Python FastAPI LangChain RAG Vector DBs OpenAI API Claude API PostgreSQL Redis AWS Azure Docker Celery

I'm open for freelance today.

Best fit:

✅ You're building an AI product and want it done right the first time

✅ You inherited a broken AI system and need someone to fix and rebuild it

✅ You're a founder who needs a technical partner for backend + AI — not just a code monkey

✅ You need async collaboration — IST timezone, works perfectly with US/Canada hours

Not a fit:

❌ You want the cheapest option

❌ You need frontend development

❌ You want a ChatGPT wrapper and call it an AI product

Rate: Tell me your scope in 2 lines. I'll give you a number same day.

Availability: Evenings & weekends IST (UTC+5:30)

Response time: Within 24 hours. Every time. No ghosting.

To reach me — just DM:

"I broke my AI system by doing ___" 😅

or

"I'm building ___ and need help with ___"

Either works. I'll respond with exactly how I'd approach it. 👇

Here's something I've noticed after 3 years of building AI systems:

Every founder thinks their AI problem is unique. It's almost never unique.

It's usually one of these 5:

Problem 1 — "Our RAG is returning wrong answers"

99% of the time: your chunking strategy is wrong. You're splitting documents by character count instead of meaning. The AI is retrieving half a thought and hallucinating the rest.

Problem 2 — "Our AI agent keeps failing halfway through"

You're not checkpointing tool call results. One API timeout and the whole task restarts from zero. I've seen this waste hours of compute daily.

Problem 3 — "Our LLM responses are getting worse over time"

You're storing conversation history and working memory in the same place. Context window is bloating silently. The model is drowning in irrelevant tokens.

Problem 4 — "Our AI app works in testing but breaks in production"

No retry logic. No fallback models. No timeout budgets. Works perfectly until real users hit it simultaneously.

Problem 5 — "We built the AI feature but the backend can't handle it"

Synchronous endpoints trying to handle LLM calls that take 10-30 seconds. Everything queues up and dies.

Sound familiar?

Drop your situation in the comments.

Tell me:

What you're building

What's breaking or what you're worried about

I'll tell you exactly what's wrong and how I'd fix it. For free. Right here in the comments.

No pitch. No sales call. Just real answers.

Why am I doing this?

Because the best way to show you I know what I'm doing is to actually show you I know what I'm doing.

If after reading my answer you think "this guy gets it" — then we can talk about working together.

If not, you still got free advice. Win either way.

Who I am:

3 years building production AI backends. FastAPI, LangChain, RAG pipelines, LLM integrations, AWS deployments. I've debugged the exact problems above at 2am so you don't have to.

Stack: Python FastAPI LangChain RAG Vector DBs OpenAI Claude API PostgreSQL AWS Docker

Available for: Freelance projects, part-time contracts, async collaboration

Timezone: IST — works perfectly with US/Canada hours

Rate: DM me your scope, straight answer same day

Let me ask you something.

You have an AI product idea. You've seen ChatGPT. You know what's possible.

But every developer you talk to either doesn't understand AI — or just wraps an API and disappears.

Sound familiar?

Here's what I actually do:

I build the backend that makes AI products work in production.

Not demos. Not prototypes held together with duct tape.

Real systems — where users ask questions in plain English, AI finds the right answer from your data, cites the source, and scales without breaking.

What I've shipped:

RAG pipelines that search thousands of documents in seconds

LLM integrations that actually handle edge cases and errors

FastAPI backends built async, clean, and production-ready

AI agents and automation workflows that save teams hours every week

Cloud deployments on AWS and Azure that don't fall over

Perfect fit if you're:

✅ A founder building an AI-powered product and need a solid backend

✅ A startup that needs LLMs integrated into an existing app

✅ A business that wants to automate workflows using AI

✅ Someone who needs an MVP shipped fast — without cutting corners

Not a fit if:

❌ You need full-time 9-5 availability right now

❌ You're looking for frontend work

❌ You want the cheapest option on the market

My stack:

Python FastAPI LangChain RAG Vector DBs OpenAI / Claude APIs PostgreSQL AWS Azure Docker

Availability: Evenings & weekends IST (UTC+5:30) — async-friendly, works perfectly with US/Canada clients

Rate: DM me your scope, I'll give you a straight number. No runaround.

To reach me, just DM:

"I'm building ___ and need help with ___"

I reply within 24 hours. Every time. 👇

Let me ask you something.

You have an AI product idea. You've seen ChatGPT. You know what's possible.

But every developer you talk to either doesn't understand AI — or just wraps an API and disappears.

Sound familiar?

Here's what I actually do:

I build the backend that makes AI products work in production.

Not demos. Not prototypes held together with duct tape.

Real systems — where users ask questions in plain English, AI finds the right answer from your data, cites the source, and scales without breaking.

What I've shipped:

RAG pipelines that search thousands of documents in seconds

LLM integrations that actually handle edge cases and errors

FastAPI backends built async, clean, and production-ready

AI agents and automation workflows that save teams hours every week

Cloud deployments on AWS and Azure that don't fall over

Perfect fit if you're:

✅ A founder building an AI-powered product and need a solid backend

✅ A startup that needs LLMs integrated into an existing app

✅ A business that wants to automate workflows using AI

✅ Someone who needs an MVP shipped fast — without cutting corners

Not a fit if:

❌ You need full-time 9-5 availability right now

❌ You're looking for frontend work

❌ You want the cheapest option on the market

My stack:

Python FastAPI LangChain RAG Vector DBs OpenAI / Claude APIs PostgreSQL AWS Azure Docker

Availability: Evenings & weekends IST (UTC+5:30) — async-friendly, works perfectly with US/Canada clients

Rate: DM me your scope, I'll give you a straight number. No runaround.

To reach me, just DM:

"I'm building ___ and need help with ___"

I reply within 24 hours. Every time. 👇

If you're building an AI product and your backend is a mess, or you need LLMs actually integrated into your app (not just a ChatGPT wrapper) — I can help.

Who I am:

3+ years as a Backend & AI Engineer. I recently built a production GenAI tax intelligence platform for PwC — LLM-powered document parsing, RAG pipelines, FastAPI backend, deployed on AWS/Azure. Currently building backend systems for Aditya Birla Group.

I don't just write code. I've shipped real AI products for real enterprise clients.

Where I add the most value:

🔥 You have an AI idea but no backend — I'll architect and build it from scratch

🔥 You have a backend but need LLMs/RAG integrated properly

🔥 You need APIs built fast, clean, and production-ready

🔥 You want an AI agent or automation workflow that actually works

My stack:

FastAPI Python LangChain RAG Vector DBs OpenAI/Claude APIs PostgreSQL AWS Azure Docker

The pitch in one line:

Most freelancers Google "how to use LangChain" mid-project. I've already done it in production, for a Big 4 firm.

Availability: Part-time / async — evenings & weekends IST (UTC+5:30). Works great with US/Canada clients.

Rate: DM me with your scope — I'll give you a straight answer, no fluff.

👇 Comment or DM — tell me what you're building in 2 lines. I'll respond within 24