I take responsibility for the whole system: technical architecture, cost, deployment, and long-term operability.
Structured discovery sessions with operational and technical stakeholders to identify high-value bottlenecks, quantify impact, and translate ambiguous requirements into a shortlist of well-defined AI opportunities.
End-to-end architectures with explicit trade-offs across latency, cost, and reliability. Forecasts for AI usage, inference, storage, and infrastructure costs before a single line of production code is written.
Agent orchestrators with tool-calling, structured outputs, and human-in-the-loop checkpoints. MCP integration layers that expose platform capabilities as callable tools to enterprise systems.
Retrieval systems over enterprise documents, legal corpora, product catalogues, and internal knowledge bases - with hybrid search, re-ranking, and grounding strategies that minimise hallucination risk.
End-to-end custom ML delivery - data gathering, labelling analysis, experimentation, evaluation, and deployment. Transformer-based classifiers, forecasting models, and specialised inference engines.
Profiling, cleaning, standardisation, validation, risk analysis, issue detection, and anomaly reporting - designed as reusable modules over high-value enterprise data.
Production-grade APIs designed for reliability and observability, with asynchronous pipelines capable of high-throughput processing and clean integration into existing enterprise stacks.
Containerised services, reproducible builds, and deployment workflows that take a working prototype to a hardened production system with monitoring, logging, and rollback discipline.
Cloud architecture aligned to user volume, latency targets, compliance constraints, and cost envelopes. Right-sizing compute, managing inference costs, and planning for scale from day one.