// Software Development Engineer · Amazon
Siddartha
Darisi
that makes AI reliable at enterprise scale.
// About
I'm a Software Development Engineer at Amazon in Seattle, on the AWS Quick RAG team — building high-throughput ingestion pipelines for Amazon Quick Suite, Q Business, and Kendra that process 10,000+ documents a day and power RAG and LLM experiences over enterprise data.
Before Amazon, at People Tech Group I built GenAI-driven MLOps that turn natural-language prompts into validated Infrastructure-as-Code, and an AI/ML document pipeline for HCA Healthcare. I hold an MS in Cyber Security & Privacy (NJIT) and four certifications — two AWS Associate, CompTIA Security+, and CEH v12 — and I'm currently deepening my AI/ML expertise through graduate study at Indiana Wesleyan University.
I mentor engineers on GenAI and DevOps practices. My north star: reliable, responsible AI at enterprise scale.
- docs/day processed by pipelines I build at Amazon
- 0k+
- docs/day processed by pipelines I build at Amazon
- certifications — AWS ×2, Security+, CEH v12
- 0
- certifications — AWS ×2, Security+, CEH v12
- graduate programs — MS Cyber Security + AI/ML (in progress)
- 0
- graduate programs — MS Cyber Security + AI/ML (in progress)
- engineers mentored in GenAI & DevOps
- 0
- engineers mentored in GenAI & DevOps
Personal Value Proposition
As a software engineer specializing in cloud architecture and AI/ML systems, I help organizations turn raw enterprise data into intelligent, production-grade products. From high-throughput ingestion pipelines processing 10k+ documents a day to GenAI-driven MLOps that cut release cycles by 35%, I build the infrastructure that makes AI dependable at scale — and I share what I learn by mentoring engineers and publishing hands-on resources like the ones in this portfolio.
// Target audience · Written for AI/ML hiring managers and technical recruiters evaluating my work, engineering collaborators and mentees, and fellow AI/ML practitioners and learners.
// Experience
2024 — Present
Software Development Engineer
Amazon · Seattle
AWS Quick RAG team — high-throughput ingestion for Quick Suite, Q Business, and Kendra: 10k+ docs/day, zero-downtime re-ingestion, 60% faster ML deployments.
Apr — Oct 2024
Cloud Engineer
People Tech Group · Seattle
GenAI-driven MLOps turning natural-language prompts into validated IaC (35% faster releases); AI document pipeline for HCA Healthcare cutting manual entry 80%+.
// Portfolio Artifacts
View all →Haven — Anatomy of a Listening Machine
A fully local AI therapy companion — QLoRA fine-tuned Llama, a trained 28-emotion classifier, retrieval-grounded answers, and hands-free voice — every step of the ML lifecycle on an 8 GB laptop GPU. Interactive build log with demo video on its page.
The Climate of Machine Intelligence
Interactive AI/ML history timeline (1943–2025) charting booms, winters, and thaws — 40 milestones, era barometer, and a log-scale training-compute visualization.
Triage Copilot — On-Call AI Assistant
An AI agent that advises but never executes — it questions its way to a hypothesis for a failed AWS pipeline and refuses to guess. Built for a 3am page. Live demo on its page.
Machine Learning vs. Deep Learning — A Decision Framework
One question decides between them: can a human write the predictive features down in advance? Two case studies — telecom churn and diabetic retinopathy — where the losing approach was actually tried.
PrepPilot — AI Mock Interview Tutor
A local-first AI interviewer that listens to how you speak, not just what you say — a full voice loop (VAD → Whisper STT → prosody analytics → LLM coaching → TTS) running on an 8GB laptop GPU. Demo video on its page.
Let's build something intelligent
Open to conversations with recruiters, collaborators, and fellow builders about AI/ML, cloud infrastructure, and MLOps.
or ask my AI assistant — the chat bubble in the corner