Architecting Intelligent Systems.

Focused on making high-quality AI accessible to Bangladeshi students, enabling more effective, engaging, and enjoyable learning experiences.

• —Architecting AI-powered learning systems tailored to the Bangladeshi education context
• —Advising on LLM integration, curriculum design, and responsible AI adoption

Led the Core RAG & AI Team, architecting and shipping a production-ready RAG platform handling real-world workloads without vendor lock-in.

• —Led Core RAG & AI Team — owned end-to-end architecture from retrieval design to agent orchestration
• —Built production-ready RAG system without vendor lock-in, handling real-world enterprise workloads at scale
• —Designed multi-agent workflows with explicit state management, improving system throughput by 30%
• —Developed advanced agentic RAG with multiple memory layers (short-term, long-term, semantic), increasing contextual accuracy by 45%
• —Implemented time-aware RAG with temporal context understanding for recency-based retrieval and dynamic data
• —Developed web search integration with deep answer feature combining multiple sources, fact-checking, and synthesis
• —Implemented full-fledged citation system with proper referencing across files, web sources, and memory layers
• —Reduced hallucinations by 98% using hybrid search, reranking models, and strict source-grounding strategies
• —Built internal evaluation benchmark, cutting evaluation time by 99% and enabling rapid model iteration
• —Scaled system from 100 to 10,000+ concurrent users with latency optimizations achieving ChatGPT-level response speeds
• —Developed image generation and editing pipelines with pixel-level control, integrated into agent workflows

Progressed from Trainee Engineer to ML Engineer over 1 year 10 months, building production-grade LLM integrations and private AI deployments for enterprise clients.

• —Designed Port and Adapter (Hexagonal) architecture for seamless multi-provider LLM integration (OpenAI, Anthropic, local LLMs)
• —Deployed LLM evaluation pipeline with source-based fact-checking, improving response reliability by 35% and user trust by 25%
• —Reduced AI safety and jailbreaking risks by 45% through multi-layer guardrails and advanced prompt engineering
• —Delivered private on-premise AI solution for enterprise client — air-gapped, reducing cloud dependency and latency by ~30%
• —Utilized Docker and self-hosted GPU infrastructure, cutting model startup time by 20–25%
• —Built full RAG application (3M+ sample database) using open-source LLMs, Next.js, LangChain, and FastAPI as Trainee

Joined as intern, progressed to Researcher & Engineer. Built conversational AI systems and contributed to Bengali language NLP research.

• —Built Rasa-based chatbots for financial services and mobile operator industries (PoC and pilot deployments)
• —Developed multilingual restaurant chatbot supporting English, Banglish, and Bangla natural language inputs
• —Collaborated on Bengali Automatic Speech Recognition (ASR) tool — high-accuracy speech-to-text conversion
• —Researched Voice Activity Detection (VAD) technologies to improve system efficiency and responsiveness
• —Served as Data Annotation Team Lead, managing NLP dataset collection, labeling, and quality assurance

Early-career data science role working on OCR, text summarization, data visualization, and analytical reporting across client projects.

• —Evaluated and compared OCR libraries, contributing to a real-world document processing tool
• —Researched and applied modern text summarization algorithms to real-world client data
• —Built custom Google Data Studio dashboards for client data communication and stakeholder reporting
• —Pre-processed and cleaned complex datasets, designed interactive dashboards, and performed web scraping