You will get expert AI training data, RLHF evaluation, and data annotation
Rising Talent
Rising Talent
Project details
I provide expert AI training data services backed by 20 years of enterprise software engineering. Unlike generic data annotators, I bring real production experience across semiconductor, airlines, supply chain, energy, e-commerce, and SaaS domains.
My services include RLHF (Reinforcement Learning from Human Feedback) where I evaluate, rank, and improve AI model outputs for code generation and technical content. I assess AI-generated code for correctness, best practices, security, and production-readiness across .NET, TypeScript, React, Node.js, and Python.
I also deliver text and NLP annotation — sentiment analysis, intent classification, named entity recognition — in English, Hindi, and Punjabi. For teams building custom models, I create structured labeled datasets tailored to your specific requirements.
Why me: I evaluate AI outputs the way a senior engineer does in a code review — because that is exactly what I have done for 20 years across Fortune 500 clients. I am not labeling data entry.
Tech: C#.NET, TypeScript, React, Node.js, Python, PostgreSQL, Redis, Docker, Azure, AWS, Clean Architecture, CQRS, DDD, microservices.
My services include RLHF (Reinforcement Learning from Human Feedback) where I evaluate, rank, and improve AI model outputs for code generation and technical content. I assess AI-generated code for correctness, best practices, security, and production-readiness across .NET, TypeScript, React, Node.js, and Python.
I also deliver text and NLP annotation — sentiment analysis, intent classification, named entity recognition — in English, Hindi, and Punjabi. For teams building custom models, I create structured labeled datasets tailored to your specific requirements.
Why me: I evaluate AI outputs the way a senior engineer does in a code review — because that is exactly what I have done for 20 years across Fortune 500 clients. I am not labeling data entry.
Tech: C#.NET, TypeScript, React, Node.js, Python, PostgreSQL, Redis, Docker, Azure, AWS, Clean Architecture, CQRS, DDD, microservices.
Machine Learning Tools
BERT, ChatGPT, GitHub Copilot, GPT-3, NLTK, NumPy, pandas, Python, SQL, TensorFlowWhat's included
| Service Tiers |
Starter
$200
|
Standard
$500
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 20 days |
Number of Revisions | 0 | 1 | 2 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$180
Additional 10 hours annotation
+$180
Hindi Punjabi bilingual annotation
+$100
Team training on annotation guidelines
+$300Frequently asked questions
About Techscriptaid
Principal Software Architect | AI Agent Builder | 20 Years Enterprise
Jaipur, India - 8:59 pm local time
Principal Software Architect with 20 years of enterprise systems experience across Fortune 500 engagements — Applied Materials (semiconductor) through ASM Technologies, PepsiCo (supply chain) through Melstar/Genpact, and United Airlines / Virgin Atlantic (aviation revenue management) through RTS Systems.
I hold an MBA in Business Analytics from BITS Pilani, which means I bridge deep technical execution with senior business stakeholder strategy — the cross-functional translation layer between data science, engineering, and executive leadership.
I architect and ship AI-integrated production systems — not demos.
Three products in active production: VoiceMeet Pro (scalable real-time audio conferencing with WebRTC/LiveKit, cloud recording, AI transcription, live on Google Play), ScriptPost AI (autonomous WordPress content engine powered by local LLMs — Ollama / Qwen 2.5 Coder 14B — at zero API cost), and Spark (Python multi-agent evaluation system on FastAPI with concurrent observer agents and atomic state coordination).
My core work is the architecture and MLOps layer that takes data science models from notebook to production: model serving infrastructure, multi-agent orchestration, feature integration, BI/dashboard wiring, observability, and governance.
I partner with applied data scientists on the modeling; I own the platform that makes it reliable at scale. Active development environment: multi-model stack — local Ollama (Qwen 2.5 Coder 14B, Gemma) plus Anthropic API selected per task, with Claude Code integrated in VS Code.
Onsite client work in the US (2017, Applied Materials) and Australia (2012, TridentGlobal).
Founder of TechScriptAid™ (D-U-N-S® 581452107).
PRODUCTS SHIPPED
▶ VoiceMeet Pro
Real-time audio meeting platform. Scalable multi-participant rooms, sub-2s latency, studio-grade WebRTC audio (LiveKit), per-participant cloud recording, AI transcription with real speaker names (Whisper via Groq), host visibility controls, guest access without registration, waiting room, Google SSO, admin dashboard, Razorpay + Stripe payments. React, Node.js/TypeScript, PostgreSQL, Redis, Oracle Cloud. Live on Google Play production + browser-based.
▶ ScriptPost AI
Autonomous WordPress content engine powered by local LLMs (Ollama/Qwen 2.5 Coder 14B). Generates 1,500–2,000 word SEO-optimized articles with syntax-highlighted code blocks, auto-fixes 14+ SEO issues, generates structured data schema (JSON-LD) and meta descriptions, intelligent internal linking, batch processing with cron scheduling, error recovery with exponential backoff. TypeScript, Node.js, Python tooling, Clean Architecture, DDD. Zero API costs.
▶ Spark
Python multi-agent evaluation system on FastAPI + React/TypeScript Vite. Orchestrates five concurrent specialist agents via asyncio.gather after each interaction, with atomic JSON state patching through asyncio.Lock (read-inside-lock semantics), crash-safe SQLite task queue with replay-on-startup recovery, SSE streaming chat, and a three-pass JSON extractor with a documented switch trigger to tool-use enforcement when observer failure-rate data justifies the per-call token cost. Schema-versioned profile model with automatic migration. Production-grade Python concurrency and evaluation harness design.
SERVICES
AI integration — RAG pipelines, LLM workflows, multi-agent orchestration, intelligent automation
AI evaluation — evaluation harness design, structured output validation, model output analysis
Full-stack development — Python/FastAPI, Node.js/TypeScript, React/Next.js, PostgreSQL, Redis
Cloud & deployment — Oracle Cloud, AWS, Docker, automated SSL, CI/CD pipelines with architecture fitness checks
System architecture — Clean Architecture, CQRS/MediatR, Circuit Breakers, DDD
Legacy modernization — .NET/Java monoliths → modern Python or Node.js/TypeScript stacks
TECH STACK
Python, FastAPI, asyncio, TypeScript, Node.js, React, Next.js, PostgreSQL (+ pgvector), Redis, SQLite, Docker, Oracle Cloud, AWS, LiveKit, Ollama (Qwen 2.5 Coder, Gemma), Anthropic API, OpenAI API, Whisper, C#, ASP.NET Core. Claude Code in VS Code as primary dev environment.
ENGINEERING APPROACH
Clean Architecture. CQRS with MediatR. Circuit Breaker patterns. Atomic state coordination for concurrent systems (asyncio.Lock with read-inside-lock semantics). Crash-safe task queues with replay. Redis caching and session management. Docker containerization. Automated CI/CD pipelines with dependency-cruiser architecture fitness checks. Dependency Injection. Repository Pattern. Transactional reliability.
Comprehensive error handling, structured logging, and monitoring. Full documentation and knowledge transfer.
Steps for completing your project
After purchasing the project, send requirements so Techscriptaid can start the project.
Delivery time starts when Techscriptaid receives requirements from you.
Techscriptaid works on your project following the steps below.
Revisions may occur after the delivery date.
Discuss requirements and scope
We align on data type, domain, volume, quality benchmarks, and timeline. I ask clarifying questions to ensure accurate annotation from day one.
Create annotation guidelines
I develop detailed labeling rules with examples, edge cases, and quality criteria specific to your dataset and model requirements.