Architect, build, and manage end-to-end AI/ML platforms spanning data ingestion, feature engineering, model training, fine-tuning, deployment, monitoring, and lifecycle management.
Design and implement personalised recommendation and search systems, leveraging techniques such as graph-based models, user embeddings, and behavioural intelligence
· Explore research areas including Artificial Intelligence, Computer Vision, Deep Learning, Natural Language Processing (NLP), Reinforcement Learning, Game AI, and Speech Recognition.
Solution Guidance & Quality Assurance: Partner with solution architects and engineering leads to shape designs (batch/stream, APIs, real-time scoring, vector stores/RAG, feature pipelines) and assure conformance to blueprint pre‑build, during build, and pre‑go‑live.
Prompt & Context Engineering: Design, test, and refine complex prompts and context-management strategies to maximize the performance and reliability of LLM-powered features.
Mentor junior AI engineers, set best practices for prompt engineering, RAG pipelines, and agent evaluation, and help build reusable AI infrastructure for the broader team.
Define and implement the "Definition of Done" for AI using quantitative evaluation. Use evaluation tools (e.g. DeepEval) to score models on Faithfulness and Relevancy. Use these metrics to validate model upgrades and cost-optimisation experiments