Senior AI/ML Engineer

I advise startups and enterprises on end-to-end agentic AI strategy, architecture, and production deployment. I design modular AI components and multi-agent retrieval workflows that improve candidate and document retrieval efficiency through vector search.

I build LLM conversational agents with persistent memory, structured logging, and MCP-based tool integrations. I also deliver multimodal AI pipelines, fine-tuned LLM RAG systems, MLOps workflows, and autonomous client solutions using Claude Cowork, OpenClaw, CrewAI, and LangGraph.

I architected a RAG retrieval system using ChromaDB embeddings for knowledge-graph tasks in a government-funded cross-institutional research environment. The system was containerized with Docker and deployed through Streamlit.

I developed multimodal NLP models for classification, summarization, and generation with over 94% accuracy, fine-tuned BERT for domain tasks, and built named entity recognition models for knowledge-graph construction. I also led work on CNN and OpenCV vision pipelines, YOLO-based segmentation, and generative image systems using GANs, VAEs, and diffusion models.

I designed feature-embedding and representation-learning models for multi-domain graph data using AI and NLP techniques. My work included a multi-level graph neural network architecture that outperformed existing approaches by approximately 20%.

I built anomaly detection, content-based recommendation, and predictive analytics models, while automating machine learning training, evaluation, and optimization pipelines. I presented research internationally, led internal knowledge-sharing sessions, and mentored junior researchers during my PhD studies.

In a remote volunteer role, I led development of a real-time pipeline between a public web portal and PostgreSQL, improving operational throughput by 40% in a regulated public-sector environment.

I built a deep learning model to predict COVID-19 spread patterns across countries and visualized results with Plotly and Matplotlib. I collaborated with data science teams on PostgreSQL infrastructure, ML pipeline integration, user-data preprocessing, and statistical reporting.

I conducted research across web and software development domains, managed complex user data, and prepared data pipelines for analysis. I designed supervised and semi-supervised machine learning models for prediction and classification tasks.

I contributed to a cross-team deep learning model for crop-yield prediction that improved projected crop production outcomes by 35%. This role built my foundations in data processing, applied machine learning, and research-driven software development.

Focused on artificial intelligence, graph representation learning, natural language processing, and deployment of machine learning models for downstream tasks.

Graduated with Distinction (5.00/5.00). Focused on supervised and unsupervised machine learning model design for IT optimization.

Graduated with First Class Honours (4.50/5.00).