LLM product surfaces
Chat, copilots, and AI dashboards with streaming responses, tool calling, guardrails, and a UX that feels fast and trustworthy.
Remote AI Product Engineer From India
Hire a senior AI developer to ship LLM products and automation.
I build production AI features — RAG systems, LLM-powered product surfaces, streaming chat, and n8n automation — with the full-stack engineering to put them in front of real users, not just a demo notebook.
RAG
Retrieval systems
Streaming
LLM UX
n8n
Automation
US/UK
Remote friendly
RAG and retrieval systems
Document ingestion, chunking, embeddings, vector search, and grounded answers with citations — tuned for accuracy over demos.
Workflow and n8n automation
Internal automation, data pipelines, and AI-in-the-loop workflows in n8n so lean teams cut manual work without more headcount.
Production-grade delivery
Cost controls, caching, evals, observability, and the full-stack app around the model so it ships and stays reliable.
Why Hire Me
AI features that work in production, not in a notebook.
Artificial Mufti serves 200+ daily active users on an LLM-backed product, and Voizo runs real-time AI moderation on live calls. I build LLM features with evaluation, cost control, and fallbacks, and wire them into n8n automations and existing backends so they ship as product features rather than experiments.
Ship an AI feature that reaches real users, not a notebook demo.
Add RAG so your product answers from your own data with citations.
Automate repetitive internal work with AI-in-the-loop n8n workflows.
Add senior AI + full-stack capacity for a fixed scope or monthly contract.
Relevant Work
Proof from shipped products.
startup
Artificial Mufti
Built a two-stage RAG pipeline in Python/FastAPI: a retrieval confidence scorer filters low-certainty chunks before LLM inference, and a post-generation validation layer cross-references responses against the source corpus using cosine similarity thresholds — reducing hallucinations without increasing latency.
Next.jsReact NativePythonFastAPI
startup
Tekish
Migrated to a headless architecture: Next.js ISR for product pages (rebuilt every 60s) + serverless Node.js functions for checkout. Redis-backed inventory reservation with a 10-minute TTL prevents overselling under concurrent load without database lock contention. Deployed to Vercel Edge across 30+ regions for sub-10ms TTFB globally.
Next.jsTypeScriptNode.jsRedis
startup
Sendora
Built a Kafka event-sourced microservices architecture where each transaction is an immutable ledger event — enabling full auditability and replay without touching mutable state. AES-256 field-level encryption on PII and financial data with AWS KMS key rotation satisfies PCI-DSS requirement 3.4 without full-database encryption overhead.
React NativeNestJSGraphQLPostgreSQL
How We Work
Clear milestones, async updates, real overlap.
OpenAILangChainPythonFastAPINext.jsTypeScriptPineconen8nPostgreSQLRedis
1
Scope the AI use case
We define the exact job the model should do, the data it needs, the quality bar, the budget, and how success is measured before writing code.
2
Design the system
I choose the model, retrieval strategy, prompts, tools, and guardrails, and plan evals so quality is measurable, not vibes.
3
Build with demos and evals
You get a working AI surface each cycle with test cases, cost tracking, and async updates aligned to a US or UK schedule.
4
Launch and monitor
I deploy it, add observability and cost controls, watch real usage, and tighten accuracy and latency after users hit it.
FAQ
Questions clients usually ask.
Can you build AI features that are actually production-ready?
Yes. I build the full-stack app around the model with caching, cost controls, evals, and observability — so the feature ships to users and stays reliable, not just works in a demo.
Do you build RAG systems over private data?
Yes. I build ingestion, chunking, embeddings, and vector search so the product answers from your own documents with grounded, cited responses instead of hallucinations.
Can you set up n8n automation for my team?
Yes. I build n8n workflows and AI-in-the-loop automations for data pipelines, internal ops, and repetitive tasks so a lean team can do more without extra headcount.
Which models and tools do you work with?
OpenAI and other LLM providers, LangChain, vector databases like Pinecone, Python and FastAPI for AI services, and Next.js and Node.js for the product around them.
Available For Remote Work
Need an AI developer who can ship LLM features to real users?
RAG systems, agent workflows, n8n automations, and the APIs around them, built with evaluation and cost limits from day one. Remote from India with US and UK overlap, direct communication throughout.
Related reading.
Practical writing on LLM systems, retrieval, agents, and the automation work that turns AI into shipped product features.
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