You will get A fine-tuned open-source LLM on your domain data
Project details
Off-the-shelf LLMs don't know your domain, your tone, or your edge cases. I'll train an open-source base model on your data — your terminology, your workflows, your expected outputs — and deliver a model that performs measurably better on your specific task than any general-purpose alternative. Whether you need a clinical NER model, a customer support responder, a document classifier, or a domain-specific generator, the process is the same: clean data in, sharp model out, evaluated and packaged for your deployment environment. Your infrastructure, your data, your model.
AI Development Type
Model TuningAI Tools
Amazon SageMaker, MLflow, PyTorch, TensorFlowAI Development Language
PythonWhat's included $2,000
These options are included with the project scope.
$2,000
- Delivery Time 15 days
- Model Documentation
Frequently asked questions
About Sagar
AI and Software Product Engineer | Python | Go | Rust | AWS
Navi Mumbai, India - 6:48 am local time
What I build:
→ AI + SaaS Products — full-stack applications with LLM capabilities baked in from day one, not bolted on later. Built for real users, real scale, real businesses
→ LLM Applications & AI Agents — RAG pipelines, document intelligence, agentic workflows, custom chatbots — production-grade, not proof-of-concept
→ Custom ML Models & LLM Fine-tuning — when off-the-shelf models aren't enough, I build and fine-tune for your domain, your data, your edge cases
→ Fast backends in Python (FastAPI, LangChain) or Rust when throughput and latency actually matter
→ Clean frontends in React or Svelte — interfaces that make AI feel intuitive, not clunky
→ Cloud infrastructure on AWS — containerized, scalable, production-hardened
→ Enterprise Architecture — system design for complex organizations, multi-service integration, data migration at scale, performance optimization
→ Prototypes & MVPs — for founders who need to validate fast or walk into a board room with something real
Industries I've shipped in:
US Healthcare — HIPAA-aware architectures, clinical data pipelines, AI-assisted workflows
Airlines — operational systems, real-time data processing, reliability-critical infrastructure
Payments — high-throughput transaction systems, fraud detection, compliance-aware design
IT Infrastructure — enterprise tooling, data migrations, infrastructure modernization, observability
These aren't side projects. These are production systems handling real data, real users, real consequences.
Who I work best with:
Startups and SaaS teams who need someone to own the technical vision end-to-end — architecture to deployment — without needing a 6-month timeline and a committee. Growth-stage companies integrating AI into existing products. Enterprises building AI divisions, migrating data at scale, or modernizing legacy infrastructure where getting the architecture right from day one is non-negotiable.
If you need one person who has seen the full picture — from fine-tuning a domain-specific model to deploying the SaaS that runs on top of it — that's the work I do.
My background:
IIT Delhi (Master's, AI & Quantum Communication) + 10+ years across the full stack. I've worked as a pure enterprise architect, built and recruited entire AI engineering teams for clients, fine-tuned LLMs on domain-specific data, run complex data migrations, and shipped SaaS products from zero to production.
I think in products and systems, not just code. That means I'll tell you when a simpler solution is better, when you don't need a custom model, and when the real bottleneck isn't technical at all. I've sat on both sides of the table — as an engineer and as a product builder — and that changes how I approach every engagement.
Stack:
Python · Rust · Go · FastAPI · LangChain · LlamaIndex · React · Svelte · AWS · PostgreSQL · Elasticsearch · PyTorch · Docker · Kubernetes · Vector DBs · OpenAI / Anthropic APIs
How I work:
→ Clear scoping upfront — you'll know exactly what's being built before a line of code is written
→ Direct communication, no jargon, no vanishing mid-project
→ I flag problems and tradeoffs early, not after the deadline
→ Comfortable working async across US timezones
Ready to build? Send me a message describing what you're working on — even a rough idea — and I'll tell you honestly how I'd approach it and whether I'm the right fit.
Steps for completing your project
After purchasing the project, send requirements so Sagar can start the project.
Delivery time starts when Sagar receives requirements from you.
Sagar works on your project following the steps below.
Revisions may occur after the delivery date.
Data audit & preparation
I review your dataset for quality, volume, and format suitability. Raw data is cleaned, deduplicated, and structured into the correct training format. If gaps exist in the data, I flag them before training begins.
Base model selection & training config
I select the optimal base model for your use case and compute budget, configure the fine-tuning approach, set hyperparameters, and run a small pilot training job to validate the pipeline end-to-end before committing full compute.