ML / Agentic AI
Production-grade AI architectures
I design AI systems that are grounded in enterprise architecture: clear boundaries, robust integration, observability and cost control. The goal is not just a demo, but a capability that can be governed, audited and evolved.
Capabilities
From requirements to AI architecture
LLMs & tools (MCP)
- Translate business requirements into LLM-centric architectures.
- Design tool ecosystems using MCP and robust API contracts.
- Define guardrails, permissions and observability for tool usage.
Foundry AI
LangChain
MCP
RAG & data access
- Design RAG pipelines with vector DBs and hybrid retrieval.
- Run ML/AI where data resides to minimise latency and egress cost.
- Integrate with existing data platforms (e.g. Snowflake, Databricks).
Azure AI & observability
- Azure AI Search, OpenAI, App Service and serverless hosting.
- Application Insights for logs, metrics and tracing.
- Security, RBAC, VNets and zero trust patterns.
Patterns
Reference patterns I apply
Agentic workflows
- Task decomposition and orchestration across tools and services.
- Clear separation between reasoning, tools and data access.
- Fallback and recovery strategies for unreliable components.
Enterprise integration
- APIs, events and batch processes aligned with AI use cases.
- Integration with CRM/ERP and HR/Finance systems.
- Standards-based interfaces (Kafka, K8s, OAS, ESBs, iPaaS).
Governance & risk
- Risk identification and mitigation for AI features.
- Traceability from requirements to AI components.
- Monitoring, reporting and KPI definition for AI value.
AI Governance Frameworks
- A certifiable standard (ISO 42001)
- A risk framework (NIST)
- Cloud‑native implementation (Microsoft RAI)
- Legal compliance (EU/UK)