Artificial Intelligence
Exceptional AI leadership harnesses machine learning, natural language processing, and generative models to drive innovation and efficiency. It demands technical mastery, ethical governance, and strategic application to solve real-world problems. By blending data engineering, model deployment, and human-AI collaboration, AI leaders transform raw data into actionable insights while ensuring scalability, security, and business alignment.
Experience
Self-Built RAG Website (2025)
Developed a fully custom RAG-indexed self-promotion website using Django and Python. Leveraged pgvector for vector database storage, Hugging Face for embeddings, and OpenAI GPT-3.5 for LLM generation. Designed end-to-end pipeline for semantic search and dynamic content retrieval, hosted on Render for under $10/month — demonstrating hands-on AI deployment from concept to production.
InvoCare – LLM Integration for Error Log Analysis
Integrated a Large Language Model (LLM) into support functions via the Microsoft Copilot trial, generating human-readable error logs. Accelerated ticket triaging and reduced diagnostic time, proving AI’s practical value in operational troubleshooting.
Credentials
- Azure AI Fundamentals (Microsoft) – Covers AI workloads, machine learning principles, computer vision, and natural language processing. Emphasises ethical AI, responsible deployment, and integration with Azure services like Cognitive Services and Azure Machine Learning. Key learnings: designing scalable AI solutions, bias mitigation, and compliance with data privacy standards.
- Oracle Cloud Infrastructure 2025 Generative AI Professional – Focuses on OCI Generative AI service, prompt engineering, fine-tuning, and deployment. Includes building AI agents, RAG pipelines, and multimodal models. Key learnings: leveraging OCI’s AI infrastructure for enterprise-scale applications, security best practices, and cost-optimised scaling.
Key Learnings
- Prompt Engineering: Crafting precise inputs for LLMs to elicit reliable outputs, balancing specificity with flexibility.
- Embeddings & Vector Search: Using Hugging Face models to convert text into semantic vectors, stored in pgvector for efficient similarity search.
- Ethical AI: Addressing bias, privacy, and transparency — essential for production deployments.
- Hybrid Deployment: Combining local (Hugging Face) and cloud (OpenAI, OCI, Azure) for cost-effective, scalable solutions.
Related Skills
Contact
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