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AI Engineer with 4+ years in Computer Vision & NLP
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MSc-qualified Machine Learning Engineer with hands-on experience building and deploying end-to-end AI systems, from model training to production inference. Proven ability to deliver Computer Vision and NLP pipelines that run efficiently on constrained hardware. Skilled in PyTorch, YOLOv8, LLMs, and Agentic AI, with a growing foundation in MLOps practices including Docker, Git, and automated reporting pipelines. Seeking an ML Engineer or AI Engineer role where I can build scalable, production-ready models.
University of Hertfordshire, UK
MSc Artificial Intelligence & Robotics (Advanced Research) · Artificial Intelligence & Robotics
September 1, 2022 – November 1, 2024
University of Calicut, India
MSc Electronics · Electronics
August 1, 2014 – September 1, 2016
University of Calicut, India
BSc Electronics · Electronics
March 1, 2010 – March 1, 2013
Nippon Data Systems Ltd
Rollout Support Engineer
October 1, 2020 – March 1, 2022
India
Actionfi Technologies
Project Engineer – IoT Products
June 1, 2017 – October 1, 2020
India
Workplace Safety Monitoring System
January 1, 2025 – Present
Designed and deployed a real-time Computer Vision pipeline using YOLOv8 and OpenCV to detect PPE non-compliance (helmets, vests, masks) across multiple simultaneous live CCTV streams. Optimised model inference to achieve 25+ FPS on non-GPU server hardware, eliminating the need for cloud GPU instances and reducing client infrastructure costs. Built a Python backend with SQLite event logging and automated SMTP email alerts, enabling instant violation notifications to site managers. Generated automated weekly compliance reports using Pandas and NumPy, reducing manual safety auditing effort by 50%.
Explainable Credit Scoring System
January 1, 2024 – December 1, 2025
Built a complete loan default prediction pipeline using Logistic Regression, Random Forest, XGBoost, and LightGBM; achieved a peak AUC-ROC of 0.8658. Integrated an LLM/Agentic AI layer to extract context from unstructured client profiles, enriching tabular model features and improving prediction accuracy. Applied fairness-aware preprocessing (KNN imputation, outlier capping) and Explainable AI (SHAP feature importance, LIME) to ensure transparent, auditable outputs. Delivered automated Excel reporting with ROC curves and SHAP charts for non-technical stakeholders.
Wearable Blindness Assistance System
January 1, 2022 – December 1, 2024
Architected a lightweight YOLOv5-based Computer Vision pipeline in PyTorch for real-time object detection and proximity awareness for visually impaired users. Achieved sub-300ms end-to-end latency from obstacle detection to Bluetooth audio alert output. Applied OpenCV frame normalisation and a Python frame-skipping loop to maintain stable tracking across volatile indoor/outdoor lighting at human walking speeds.
View ProjectGetting Started with Docker
Simplilearn
May 1, 2026 – May 1, 2026
Cloud Computing Fundamentals
Simplilearn
January 1, 2025 – Present
Agentic AI
Hugging Face
January 1, 2025 – Present
Large Language Models
January 1, 2025 – Present
Deep Neural Networks with PyTorch
IBM/Coursera
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's project portfolio shows a diverse interest in applying AI to real-world problems, from workplace safety to assistive technologies and credit scoring. This breadth, combined with academic and professional experience in IoT and enterprise systems, suggests adaptability and a willingness to tackle varied challenges. The focus on explainable AI and fairness indicates an alignment with ethical AI development practices. The independent nature of several projects points to a self-starter mentality.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through optimizing models for non-GPU hardware and achieving low-latency performance. Their experience in automating reports and managing enterprise deployments suggests an ability to improve operational efficiency and communicate technical concepts to diverse audiences. The project descriptions indicate a proactive and independent work style, suitable for roles requiring self-direction.