AI Engineer | ML Engineer | Applied AI
LLMs · AI Agents · RAG · Machine Learning · Computer Vision · Multimodal AI
I build and evaluate AI systems that can reason, retrieve, perceive, and act. My work spans LLM engineering, AI agents, retrieval-augmented generation, machine learning, and multimodal systems, with experience across model architecture, training, evaluation, and end-to-end system design.
I’m particularly interested in turning advances in AI research into reliable, scalable systems that can solve complex real-world problems.
I’m always keen to connect, exchange ideas, and explore new conversations and opportunities across AI and technology.
London, United Kingdom
A hybrid RAG pipeline over the SciFact corpus: BM25 first-stage retrieval, ColBERT-style late-interaction re-ranking, and citation-backed generation from a 4-bit quantised Qwen2.5-3B model. The ColBERT projection layer was fine-tuned with InfoNCE on BM25-mined hard negatives.
| nDCG@10 | AP@10 | RR@10 | Re-ranking latency |
|---|---|---|---|
| 0.6858 | 0.6479 | 0.6581 | 43.1 ms / query |
A five-agent pipeline that produces investment reports from a natural-language ticker or cryptocurrency name. Components cover identifier resolution, financial data retrieval, news sentiment, and a ten-year discounted cash-flow model with WACC-based discounting. Retrieval over SEC 10-K filings uses Qdrant, and reports are generated with ReportLab.
A lightweight multimodal retrieval system that maps images to semantically related text labels without a large pretrained foundation model. A Skip-Gram backbone trained on Visual Genome was expanded with a (1+λ) evolutionary algorithm to incorporate CIFAR-100 labels, then aligned to MobileNetV3 features with InfoNCE. Image-to-text Recall@10: 91%.
An agentic system that generates recipes from a photograph of ingredients or a prepared dish, combining CLIP, Qwen2.5-3B, FAISS retrieval, Tavily, and Llama 3.
Designed ReVA, a region-aware VQA model that combines global scene context with localized visual evidence to improve fine-grained visual grounding and reduce hallucination.
ReVA connects a frozen CLIP ViT-L/14 vision encoder to Qwen2.5-7B-Instruct through separate image and region bridges. Object proposals from RAM++, spaCy, and Grounding DINO supply bounding boxes. RoI-aligned intermediate ViT features are mapped to region tokens so that answers are conditioned on local visual evidence as well as global scene context.
| VQAv2 | MMBench | SEED-Bench | POPE |
|---|---|---|---|
| 73.79% | 67.1% | 66.75% | 82.85 F1 |
To read the paper or download the implementation and weights, use the links below.
| Sep 2025 – Sep 2026 |
Master of Science, Artificial Intelligence
Queen Mary University of London, London, United Kingdom
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| Jul 2021 – Jul 2025 |
Bachelor of Technology, Computer Science and Engineering
Vellore Institute of Technology, Vellore, India
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| Jan 2025 – May 2025 |
AI Research and Development (R&D) Intern
DRDO, Ministry of Defence, Government of India, Bangalore Developed an Object Diagram Recognition system using Computer Vision and Deep Learning to interpret control-law diagram images and automatically generate executable C code. Fine-tuned YOLO and integrated efficient line-detection algorithms using OpenCV to extract precise component bounding boxes and control-flow coordinates from noisy diagram images. Constructed logical representations of diagrams as directed acyclic graphs (DAGs) and applied graph-parsing techniques for automated code generation, reducing manual coding effort and syntax errors in complex control-law diagrams. |
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| Aug 2023 – Nov 2023 |
Software Developer Intern
Pivotrics, Bangalore Developed scalable RESTful APIs for a SaaS-based payment platform to enhance customer experience, utilizing Spring Boot, WebFlux, and PostgreSQL. Optimized system performance during peak load by adopting reactive programming principles, designing non-blocking, asynchronous APIs with effective back-pressure handling for seamless management of product information. Implemented JWT-based authentication and authorisation with robust access control policies using Spring Security. |
I’m an AI/ML Engineer with experience in computer vision, multimodal AI, natural language processing (NLP), and agentic systems, passionate about building AI that can perceive, reason, and solve real-world problems.
I’m currently completing an MSc in Artificial Intelligence at Queen Mary University of London, where I design and build end-to-end AI systems spanning multimodal large language models (MLLMs), Retrieval-Augmented Generation (RAG), and multi-agent workflows. My projects range from developing vision-language models that improve visual reasoning and reduce hallucinations to autonomous AI agents capable of executing complex, multi-step tasks with minimal human intervention.
Alongside research, I apply proven ML engineering practices and inferential statistics to develop and evaluate AI systems that are scalable and production-ready, with a strong emphasis on experimentation, versioning, deployment, and maintainable software design.
Previously, as an AI R&D Intern at DRDO, I developed computer vision and deep learning systems for automated diagram understanding and image-to-code generation, gaining hands-on experience building production-oriented AI solutions.
My current interests lie at the intersection of multimodal foundation models, agentic AI, LLM reasoning, context engineering and autonomous decision-making. I’m particularly interested in building AI systems that combine perception, retrieval, planning, memory, and tool use to solve complex problems reliably and at scale.
Open to ML Engineer, Applied AI Engineer, Data Scientist, and AI Research Engineer roles.