Sarthak Agrawal

Wealth-management fintech serving the UAE and Saudi Arabia

• Built and own the Go financial-planning backend for the UAE and Saudi markets, from its API and scheduled jobs to the projection, recommendation, and data-import engines below.
• Built the retirement and goal projection engine, validated against 68 rolling 30-year S&P 500 periods (1928–2024) to within 2% of an independently written simulation; merged three diverging engine versions into one.
• Built a recommendations engine that precomputes every option’s what-if result, so the numbers always match what the client sees on click: 0 mismatches across 1,416 options on real accounts, at 18 ms per account. It replaced a 20-type system in which only 7 types ever fired.
• Built a pre-merge safety net that replays every planning-engine change against a copy of all client profiles and diffs it with production, plus a 50,000-case randomized test that money is never created or lost; together they caught bugs unit tests missed, including a seven-figure double count of retirement savings.
• Replaced random-return simulations with replays of real market crises (1929, 1973, the dot-com crash, 2008) across 7 asset classes using 1928–2025 data, exposing that the old model overstated diversification and nearly halving worst-case cushions once modelled correctly.
• Built AI document-import pipelines where the model can’t get the math wrong: the model only transcribes and labels, while Go code does the arithmetic and checks it against the statement’s running balance; 893 of 895 transactions reconciled to the smallest currency unit. Also built a monthly fund-factsheet pipeline with Claude.
• Migrated critical workflows to Temporal, eliminating 90% of unexpected failures and freeing about 3 engineering hours per day.
• Cut the release end-to-end test gate from about 80 minutes (it timed out on every release) to about 7, with 315 tests passing after fixing 48 already failing on main; made the local suite 12.8× faster (30 to 2.5 minutes) and freed about 363 CI minutes a day.

• Built a real-time stock-data pipeline with Go, Kafka, and Protocol Buffers, supporting product growth from 15K to 200K DAU in 14 weeks.
• Built RAG assistants on OpenAI APIs for support, learning, and general help; the moderation bot reduced human intervention in support queries by 90%.
• Built a personalized-feed pipeline using BERT embeddings, Milvus, GPT calls, and real-time BigQuery events for richer user vectors, increasing home-feed engagement by 40%.
• Added queue-based batching and granular session controls, reducing session-refresh database calls by 92%.
• Cut HTML build and load time from 600 ms to 60 ms with Redis caching, improving SEO performance.
• Launched a Hot News feed with read/unread and last-visit tracking, aggregating events through RudderStack and ClickHouse; tripled average news scroll count in 3 weeks.
• Designed real-time stock-tick delivery over Socket.IO with room-based, in-memory, and Redis-backed subscription modes, connected to web clients through window events.

PostTrainLLM — Mac-first LLM factory, runtime, and evaluation platform

Swift/MLX, Python, TypeScript/WebGPU, C++/WASM

• Built a local Swift/MLX pipeline for data preparation, post-training (LoRA, distillation), evaluation gates, trace capture, packaging, and OpenAI-compatible serving, with Python reference implementations and a browser WebGPU/WASM runtime.
• Implemented a KV-cached multi-turn agent runtime over OpenAI tool schemas with bounded steps, per-tool timeouts, output limits, and JSONL audit transcripts.
• Distilled a 4B specialist from ~99 frontier rollouts that scored 100% on a frontier-validated hard multi-turn tool-calling gate.

CodeVetter — Local-first verification layer for agent-generated code

Rust, SQLite, SwiftUI, MCP

• Shipped a native SwiftUI macOS workbench backed by a Rust CLI and SQLite that runs provider-neutral coding agents, persists findings with execution evidence, and drives fix, re-review, and verification-handoff loops.
• Built a read-only Rust MCP server with scoped graph/history tools, versioned resources, concurrency limits, secret filtering, bounded responses, and audit history; ~2.2 ms p50 steady queries on a 10K-event fixture.

Pace — On-device, screen-aware Mac voice agent

Swift, MLX, local LLM planners, streaming TTS

• Built an 8-step plan-act-observe loop over local planners (OpenAI-compatible servers, MLX, Apple Foundation Models) for screen-grounded click, type, scroll, and keyboard actions.
• Added model prewarming, prompt caching, screen-pixel hashing, and streaming sentence TTS, targeting a sub-500 ms first spoken response.