Hi, I'm Taif Ahmed Turjo. I'm doing my B.Sc. in Computer Science and Engineering at the
University of Dhaka.
Right now I'm a founding engineer at
Rokkhe, where we're building an
AI system that runs penetration tests on its own — it maps the target, finds a way in, proves each
finding with a working exploit, and opens a pull request with the fix.
Before Rokkhe I worked on document checking for IPO prospectuses and fund factsheets at Makebell,
and trained and reviewed code for language models at Scale AI, Turing, and SuperAnnotate. On the
side I research retrieval: my project
FedCRAG
looks at how dense retrievers forget older documents as corpora grow and change, and how small
per-client adapters can stop that without anyone sharing their raw documents.
Most of my questions sit where retrieval meets continual learning. In a RAG system the retriever is
usually trained once and then frozen, but real corpora keep moving — new filings arrive, guidelines
get revised, and different clients hold different collections. Keep fine-tuning a dense retriever on
new batches and it slowly loses its grip on older material; in a federated setting, averaging
adapters from clients with disagreeing corpora makes things worse. I want to measure that
forgetting honestly — retention matrices, backward transfer, checks that rule out optimizer noise —
and then prevent it with per-client LoRA adapters and aggregation that accounts for how severely
each client forgets. I'm drawn to the same idea elsewhere too: agents that have to prove what they
claim, whether that's an exploit for a security finding or a citation for a research report.
Building the autonomous pentesting engine: multi-agent workflows over a 60-module skill library,
a verification loop that runs each finding's proof-of-concept in a Docker sandbox before drafting a
remediation PR, and a FastAPI control plane with live agent telemetry.
Software Engineer, Makebell
October 2025 - May 2026, Remote
Built the document-intelligence and fact-checking pipeline for IPO prospectuses and fund
factsheets, then reworked it into parallel multi-agent runs that cut processing latency by 70%.
AI Trainer & L10 Reviewer, Scale AI
December 2024 - January 2026, Remote (Contract)
Fine-tuned and reviewed technical code across C, C++, Swift, Python, JavaScript, and Java, and
designed adversarial tasks that expose where models break on multi-step reasoning and tool use.
Rust LLM Engineer — PR Writer V2, Turing
September 2025 - November 2025, Remote (Part Time)
Reviewed and improved Claude's generated Rust as part of Anthropic's PR Writer V2 effort —
correctness, error handling, library habits — and reported the recurring failure patterns.
Python AI Engineer & QA, SuperAnnotate
June 2024 - Present, Remote (Contract)
Wrote training data for code models — snippets, bugs, and fixes in Python, C, C++, and HTML — and
500+ answers to programming questions used to train a chatbot.
An 8-crate system that takes a research question through plans, retrieval tasks, and evidence
checks to a grounded report. Every claim carries a SHA-256-pinned source snapshot and a resolved
citation, so a report can be rebuilt and its statements traced back to exactly where they came
from.
A platform for provisioning student VMs: a Go/gRPC orchestrator on Kubernetes with a FastAPI
gateway and SvelteKit console, persistent per-user workspaces on replicated Longhorn storage,
credit-based billing, and an admin operations suite — with a 590+ test suite in CI.
A real-time collaborative IDE: projects and file management, invitations with role-based access,
and WebSocket presence across projects — with the groundwork laid for CRDT co-editing and
Docker-sandboxed execution.
A safety app for women: SOS alerts that go out in under three seconds, GPS tracking with
safety-zone analysis, and a community feed where verified users post and help each other.