Full Stack · AI Engineering · Production Systems

AI systems thatsurvive production.

I design and ship LLM applications end to end — retrieval pipelines, vector search, typed APIs, and the frontend that makes them feel effortless. Not demos. Systems with users, uptime, and consequences.

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4+Production AI systems shipped

1M+Vectors indexed & queried

<200msp95 retrieval latency

99.9%Uptime across deployments

Selected work

Systems, not side projects.

Each of these runs in production, has real users, and taught me something a tutorial never could. Full architecture, honest trade-offs, measured outcomes.

rag-platform.system

Next.js Client

Streaming UI · optimistic state

FastAPI Gateway

Auth · rate limits · SSE streaming

Retrieval Layer

Hybrid search · re-ranking · Qdrant

LLM Orchestration

LangChain · Gemini · OpenAI fallback

PostgreSQL + Object Store

Metadata · documents · audit log

012025In production

Enterprise RAG Platform

Document intelligence that answers with citations, not hallucinations.

A retrieval-augmented generation platform that turns unstructured company documents into a queryable knowledge system. Hybrid search over Qdrant, semantic chunking tuned per document type, and grounded answers with inline source citations.

1M+

vectors indexed

<200ms

p95 retrieval

92%

answer groundedness

Challenge

Naive chunking destroyed answer quality on tables, contracts, and scanned PDFs.

Solution

Built structure-aware chunking with per-document-type strategies and overlap tuning — groundedness jumped from ~60% to 92%.

Challenge

LLM latency made the product feel broken under real load.

Solution

Token streaming end to end, retrieval-stage caching, and a re-ranking cutoff that cut p95 response time by more than half.

Outcome — Replaced hours of manual document lookup with sub-second grounded answers — adopted as the internal default for knowledge search.

Next.jsTypeScriptFastAPIPythonLangChainQdrantPostgreSQLGeminiDockerAWS

llm-workflow-engine.system

Pipeline API

FastAPI · async task graph

Step Executor

Typed I/O · retries · timeouts

Provider Router

Gemini ↔ OpenAI failover · cost routing

Observability

Per-step traces · token accounting

PostgreSQL

Run history · replayable state

022025In production

LLM Workflow Engine

Multi-step AI pipelines with retries, fallbacks, and full observability.

An orchestration layer for chained LLM operations — extraction, classification, generation — where every step is typed, retried on failure, falls back across model providers, and leaves a complete audit trail. Built because production AI fails in ways demos never show.

50K+

pipeline runs / month

99.9%

completion rate

model providers, zero lock-in

Challenge

A single provider outage took every dependent feature down with it.

Solution

Provider-agnostic step contracts with automatic failover — outages became invisible; completion rate held at 99.9%.

Challenge

Debugging a failed 6-step chain from logs alone was guesswork.

Solution

Persisted every step's input, output, and token cost as replayable state — failures now reproduce locally in seconds.

Outcome — Turned fragile prompt chains into dependable infrastructure other teams build features on without thinking about model failure.

PythonFastAPIPostgreSQLOpenAIGeminiDockerAWS

realtime-dashboard.system

React Client

Virtualized charts · websocket state

Event Gateway

FastAPI websockets · backpressure

Aggregation Layer

Rolling percentiles · drift detection

PostgreSQL

Time-bucketed history

032024In production

Realtime Ops Dashboard

Live system state for the people who get paged when it breaks.

A full stack monitoring surface for the AI platform: live pipeline health, retrieval quality drift, token spend, and latency percentiles — streamed to the browser over websockets and rendered without jank.

real-time

sub-second updates

30+

tracked signals

60fps

under full stream load

Challenge

Streaming 30+ signals froze the UI on mid-range laptops.

Solution

Batched websocket frames, virtualized rendering, and canvas-based charts — steady 60fps at full load.

Outcome — Cut incident detection from 'a user told us' to under a minute — and made token spend visible before invoices did.

ReactTypeScriptFastAPIWebSocketsPostgreSQLDocker

Experience

Where the systems shipped.

Roles measured by what went to production, not by time served.

Own the AI platform end to end: retrieval infrastructure, LLM orchestration, and the product surfaces built on top of them.

Designed and shipped the RAG platform serving grounded answers over 1M+ indexed vectors at <200ms p95
Built the LLM workflow engine handling 50K+ monthly runs with 99.9% completion across three model providers
Introduced structure-aware document chunking that raised answer groundedness from ~60% to 92%
Containerized the platform with Docker/Podman and moved deployments to AWS with zero-downtime releases

Next.jsFastAPILangChainQdrantPostgreSQLAWS

2024 — Present · Remote

Capability map

Depth over breadth.

Not a logo wall. Three domains I work in daily, and what I actually do with each tool in production.

AI Engineering

The layer where most projects die between demo and production — and where I spend most of my time.

RAG Systems

chunking strategy, hybrid retrieval, re-ranking, groundedness evals

LLM Applications

streaming, tool use, structured output, provider failover

Vector Search

Qdrant in production — indexing, filtering, quantization

LangChain

orchestration without the spaghetti

Gemini / OpenAI

multi-provider routing by cost and capability

Full Stack

Typed from the database to the pixel. The frontend is part of the system, not an afterthought.

React / Next.js

App Router, streaming SSR, real 60fps interfaces

TypeScript

strict mode, end-to-end types across the API boundary

FastAPI / Python

async services, websockets, SSE streaming

PostgreSQL

schema design, query plans, migrations that don't hurt

Infrastructure

Software isn't done until it deploys, scales, and tells you when it's unhealthy.

Docker / Podman

reproducible builds, minimal images

AWS

compute, storage, networking for the AI stack

System Design

boring architectures that survive traffic and time

Observability

traces, token accounting, latency percentiles

Now

Currently deep in agentic systems — tool-using LLM workflows with real guardrails — and pushing retrieval quality evals into CI.

Engineering philosophy

Opinions, earned in production.

Production is the only benchmark

A system that works in a notebook has proven nothing. Real users, real load, real failure modes — that's where engineering starts.

Latency is a feature

Every AI product lives or dies on perceived speed. Streaming, caching, and honest loading states are product decisions, not optimizations.

Boring architecture, interesting products

Postgres over the exotic choice. Typed contracts over cleverness. Novelty budget goes into the product, not the plumbing.

Failure is a first-class input

Models time out, providers go down, retrieval misses. Systems I ship assume all of it and degrade gracefully instead of falling over.

Contact

Let's build something that ships.

I'm open to conversations about AI platform work, full stack roles, and hard problems that need production-grade answers.

Say hello

rakeshgandla200@gmail.com