Professional Overview
As an AI Infrastructure Engineer with over 7 years of hands-on experience in building scalable systems for artificial intelligence and machine learning workloads, I specialize in designing and optimizing infrastructure that powers cutting-edge AI applications. My expertise spans AI/ML frameworks like TensorFlow, PyTorch, and scikit-learn; large language models (LLMs) such as GPT variants and Llama; generative AI (GenAI) for content creation and multimodal systems; AI agents for autonomous decision-making and task automation; and MLOps pipelines using tools like Kubeflow, MLflow, and Airflow to ensure seamless model deployment, monitoring, and scaling.I've led projects at tech giants and startups, including architecting a cloud-agnostic infrastructure on AWS, GCP, and Azure that supported training LLMs on petabyte-scale datasets, reducing training time by 40% through GPU/TPU optimization and distributed computing with Kubernetes and Ray. In one role, I developed an AI agent framework that integrated GenAI with real-time data streams, enabling predictive analytics for e-commerce platforms and boosting efficiency by 25%. My MLOps innovations have streamlined CI/CD for ML models, incorporating version control, A/B testing, and automated retraining to minimize downtime and bias.What sets me apart is my holistic approach: I bridge the gap between data scientists and DevOps teams, ensuring robust, secure, and cost-effective AI ecosystems. I'm passionate about ethical AI, contributing to open-source projects on fair ML practices. Employers should consider me because I deliver innovative solutions that drive business impact—whether accelerating R&D cycles or scaling AI to production—while adapting to emerging trends like edge AI and federated learning. Let's collaborate to build the future of intelligent systems.
Areas of Expertise
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
data science & analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
