1.4B+ tokens daily
Worked on a production AI chat system operating at substantial daily model traffic.
Applied AI freelance development
Hire an AI developer for RAG, LLM, and agent systems
Amar Tripathi is a remote applied AI freelance developer who builds RAG, LLM chat, agent, and model-routing systems. He serves US, UK, and worldwide teams from India.
Review the freelance services and selected work overview, or compare my Next.js freelance services.
A specialist for production work
I build applied AI products that connect language models with real product workflows. This work includes retrieval, streaming chat, tool calling, memory, model routing, document processing, and generated media.
At Magica.com, formerly Galaxy.ai, I led a production chat platform supporting more than 100 models from more than 12 providers. The system processes 1.4 billion or more tokens each day.
Applied AI needs more than a model call. A reliable product also needs evaluation, failure handling, observability, cost controls, data boundaries, and a clear user experience.
Production evidence
These results come from work already described in the portfolio. They show scope, scale, and engineering responsibility.
40+ AI video tools
Built reusable configuration and webhook pipelines for many generative video workflows.
6-language sandbox
Implemented sandboxed code execution within an agentic chat product.
Services
RAG and document intelligence
Build ingestion, chunking, embeddings, retrieval, source grounding, and multi-turn document workflows.
LLM chat and model routing
Create streaming conversations, provider routing, fallback logic, message persistence, and model selection.
AI agents and tool calling
Connect models to approved tools, APIs, code execution, search, and structured product actions.
Generative media systems
Build reusable workflows for image, audio, and video generation with asynchronous processing.
Remote work for US, UK, and worldwide teams
I am based in India and work remotely. Each engagement defines time-zone overlap, communication, milestones, and review points before development starts. This model supports focused delivery without implying a local US or UK office.
When this service is a good fit
This service fits products with a clear user task and accessible source data. It works best when the team can define acceptable quality, latency, and cost. I do not promise perfect model output. I design controls for uncertainty. The project also needs representative examples for evaluation. These examples help compare prompts, models, retrieval settings, and tool behavior. Sensitive data needs an agreed handling policy. Production access stays approval-gated. I record evaluation limits and known failure modes for the team before handover.
You need grounded answers from private content
Your users need answers from documents, product data, or an internal knowledge base. I can design retrieval, source display, response constraints, and evaluation cases. The system should show its evidence and avoid confident answers when retrieval is weak.
You need a multi-model AI product
Your product needs several providers, model choice, streaming responses, or fallback behavior. I can build a routing layer that separates provider details from product logic. We can track latency, errors, limits, and cost without coupling every feature to one vendor.
You need an agent with controlled tools
Your workflow needs a model to search, call APIs, run code, or update approved systems. I can define tool contracts, permission boundaries, retries, and user confirmation points. The design keeps risky actions separate from normal model generation.
How to evaluate a top freelance developer
“Best” and “top” are subjective labels. Use verifiable evidence instead. Compare candidates against these practical criteria.
Production depth
Ask how the developer handles latency, failures, limits, provider changes, data, and evaluation.
Grounded proof
Prefer concrete systems and measured results. Generic AI claims do not show reliable delivery.
Product judgment
A strong developer should explain when AI helps, when it adds risk, and where simpler code works better.
Engagement process
Step 1
Use-case definition
We define the user task, source data, expected output, risk level, and success checks.
Step 2
System design
I map the model, retrieval, tool, storage, evaluation, and interface requirements.
Step 3
Bounded implementation
I build the smallest useful workflow first. Each milestone has observable acceptance checks.
Step 4
Evaluation and handover
We test quality, latency, failures, and cost behavior. I document limits and operating needs.
Common questions
Can I hire Amar Tripathi as a freelance AI developer?
Yes. I accept suitable remote AI projects and contracts. I work from India with agreed time-zone overlap.
What is RAG development?
Retrieval-augmented generation finds relevant source material before a model answers. It can improve grounding when the retrieval and evaluation are sound.
Which AI systems can Amar build?
I build RAG products, streaming chat, model routing, agents, tool calling, document workflows, code execution, and generative media systems.
Can Amar integrate several LLM providers?
Yes. My production work includes routing across more than 100 models and more than 12 providers.
How should a team choose the best AI freelancer?
Check production evidence, evaluation methods, failure handling, data safeguards, cost awareness, and product judgment. Avoid unsupported ranking claims.
Need a different technical focus?
Review my Next.js freelance services for a related service, or send a short project brief.