5
3
Bangladesh
4+ years of experience
I am an AI-focused Software Engineer with a strong interest in Artificial Intelligence, Machine Learning, and modern software engineering. My experience includes building LLM-powered applications, RAG systems, AI agents, and model fine-tuning workflows using Python and other programming technologies. I enjoy turning AI concepts into practical, real-world applications and exploring technologies such as LangChain, LangGraph, MCP, local LLMs, and agentic AI systems. Alongside AI engineering, I am expanding my expertise in backend development, Rust, and desktop application development. I am passionate about continuous learning, problem-solving, and building reliable, useful software that combines AI with strong engineering practices.

First-round interviews don't scale. Recruiters lose days scheduling phone screens, every candidate gets different questions, and decisions come down to gut feeling — while great candidates accept faster offers elsewhere. InterviewCoach replaces the entire first round with a structured AI voice interview. An employer signs up, describes the role (JD, topics, pass score), and gets an AI-drafted question bank with scoring rubrics plus one auto-generated, shareable interview link. A candidate opens the link — no account, no scheduling — and talks to Talha, a live AI interviewer powered by the AssemblyAI Voice Agent API (real-time STT, managed LLM, TTS, barge-in). A video avatar listens while they speak and talks back; every answer is scored on clarity, depth, and STAR structure with instant spoken coaching. When the interview ends, the verdict appears immediately: beat the bar and a personal next-round pass link is auto-issued on the spot. Employers review transcripts, scores, pass rates, and passes in the hiring dashboard. Built with: AssemblyAI Voice Agent API (voice pipeline + turn detection), Python stdlib + SQLite backend (zero pip dependencies), Mistral AI question drafting with a role-aware local fallback, and a tool-call loop (get_question → evaluate_answer → get_session_summary).
30 Sep 2026

The problem. Every free file converter on the internet makes you upload private presentations to someone else's server — a privacy risk wrapped in paywalls, watermarks, queues, and forced accounts, all for a 10-second task. Desktop suites are heavy overkill, and true conversion normally demands expensive server infrastructure. The solution. SlideShift flips the model: the converter is the webpage. Drop a .pptx and get a clean PDF; drop a .pdf and get a real .pptx — with drag & drop, paste support, quality and layout options, live progress with a per-slide log, and one-click download. Direction is auto-detected from the file, and a conversion counter in localStorage tracks usage without accounts. How it works. Four client-side engines do everything: PDF → PPTX renders each page to an image with PDF.js and places it full-bleed on its own slide with PptxGenJS (16:9 or 4:3). PPTX → PDF unzips the .pptx in-browser with JSZip, reads slides in true presentation order, extracts text and images, and rebuilds a styled PDF with jsPDF. Open DevTools → Network during a conversion: zero file traffic. Files never leave the tab. Honest engineering. This is a content-preserving converter, not a pixel-perfect clone — stated openly on the site: .pptx only (no legacy .ppt), PPTX → PDF keeps text and images but not exact fonts, animations, or charts, and PDF → PPTX slides are faithful images rather than editable text. No password-protected PDFs. Why it matters. Because there is no backend, no database, and no build step — just public/index.html, styles.css, app.js plus CDN libraries — the entire app deploys on free static hosting forever, costs nothing to run, and can't leak your files because it never receives them.
27 Sep 2026

CineCaption AI is a video captioning agent built for the AMD Developer Hackathon Track 2. It downloads a video clip, extracts eight evenly-spaced key frames using ffmpeg, and sends them to Kimi K2.6 — a vision-language model on Fireworks AI — which produces a detailed factual description of the scene. That description is then passed to DeepSeek V4 Pro, a text model that generates four styled captions: formal, sarcastic, humorous-tech, and humorous-non-tech. The pipeline processes multiple videos in parallel using ThreadPoolExecutor, completing each clip in 30-60 seconds. It includes automatic retry logic, JSON parsing with fallback, and a per-style fallback caption generator to guarantee output for every requested style. The entire agent runs inside a lightweight Docker container (python:3.11-slim + ffmpeg) and is deployed to GitHub Container Registry with a linux/amd64 manifest.
13 Jul 2026