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Ankit A.
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Ankit A.

Senior AI Engineer | LLM, RAG & Generative AI | Python Full-Stack

There's a difference between an AI integration and an AI system. Most projects deliver the first. Clients pay for the second. An AI integration means you called the OpenAI API and got a response. An AI system means the response is accurate, the latency is acceptable, the cost per query is sustainable, it doesn't break when the API rate-limits you, and it still works when your user sends something completely unexpected. These are not the same thing. ━━━ WHAT BREAKS AI PLATFORMS AT SCALE ━━━ → Context window mismanagement You pass the full conversation history every call. At 1,000 users, your token costs are 10x what you budgeted. At 10,000, the system collapses. → No streaming Users wait 8 seconds for a response. They leave. Streaming is not optional on conversational products. → No retry architecture OpenAI returns a 429. Your app throws an error to the user instead of queuing and retrying. → Real-time without proper WebSocket management Connection drops are not handled. Users see a frozen screen and assume the product is broken. → No cost monitoring You discover you spent $4,000 in API costs on a feature you thought would cost $40. I architect systems where none of these surprises exist. ━━━ THE STACK THAT PREVENTS THESE PROBLEMS ━━━ → Streaming responses via WebSockets from day one → Token budgeting per user, per session, per feature → Retry queues with exponential backoff (Celery + Redis) → Rate limit handling built into the API layer → Real-time observability on every LLM call cost, latency, token count, model used ━━━ CORE STACK ━━━ Backend → Python · Django · FastAPI · Flask · Node.js AI Layer → OpenAI · Claude · LangChain · RAG · Pinecone · Weaviate Real-Time → WebRTC · WebSockets · Twilio SMS/Voice/Video Frontend → React · Next.js · TypeScript Database → PostgreSQL · MongoDB · Redis Cloud → AWS (Lambda · EC2 · S3) · Docker · Celery · RabbitMQ ━━━ INDUSTRIES SHIPPED ━━━ Healthcare · FinTech · EdTech · Fitness & Sports Food Delivery · IoT & BLE · eCommerce · Dating & Matrimonial ━━━ WHAT YOU CAN EXPECT ━━━ → No ghosting. No guesswork. → Hyper-structured weekly updates → Technical clarity on every decision → Architecture reviews before we write a single line ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ What's the part of your system you're most worried about when user numbers grow? Start there. I'll tell you what breaks first.

🇮🇳Mohali, India$25/hr100% JSS4.84 (3)Since Mar 2023

Earnings and projects

MRR
$235

Last 6 months ÷ 6

World#107,598 / 412k
🇮🇳Country#11,332 / 45.9k
Revenue, last 30 days
$0

Last 6 months: $1.4k

Total earnings
$55k

Billed in all: $58k

World#70,062 / 412k
🇮🇳Country#10,605 / 45.9k
Per project
$4.6k

$353 in the last 6 months

World#37,682 / 412k
🇮🇳Country#4,194 / 45.9k
Projects, last 6 months
4

Across 5 contracts

20% fixed80% hourly
World#51,813 / 412k
🇮🇳Country#6,157 / 45.9k
Projects in all
12

Across 15 contracts

7% fixed93% hourly
World#183,649 / 412k
🇮🇳Country#24,811 / 45.9k

Earnings, last 30 days

$58.2kNothing earned in the last 30 daysBilled in all, before Upwork's fee · drag across the chart to see what was earned in any stretch
$57k$58k$59k$60kOct 8Oct 23Nov 7

Skill rankings: Top 15% in PyQt

Ranked by MRR among everyone on Upwork with the skill.

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  6. #307AI Platform816 freelancers · 🇮🇳 #102Top 38%
  7. #323NodeJS Framework1,512 freelancers · 🇮🇳 #104Top 21%
  8. #433Flask2,438 freelancers · 🇮🇳 #102Top 18%