Data and AI Architect
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Summary
A Data & AI Architect role assessing an enterprise client's data and AI platform readiness and designing end-to-end solutions across data, ML, generative AI and agentic AI. Day to day: platform gap analysis, solution architecture and reusable design patterns, plus hands-on technical leadership from design through production delivery.
Enterprise platform strategy and readiness
- Experienced Data & AI Architect with a track record of designing and delivering enterprise-scale data, machine learning, generative AI and agentic-AI solutions.
- Assesses enterprise platform readiness across the full data and AI landscape, including data foundations, ML and LLM services, enterprise knowledge, integration, AI controls, user experience, runtime orchestration and platform engineering.
- Identifies platform gaps, constraints and technical risks; develops practical, prioritised recommendations to strengthen the client’s data and AI platform.
- Defines end-to-end solution architectures for specific data, ML and AI use cases, balancing functional needs with the client’s existing technology environment, architecture standards, security requirements and delivery constraints.
- Designs reusable patterns for data access, retrieval and knowledge grounding, model and agent execution, enterprise integration, identity and access controls, observability, evaluation, resilience and operational support.
- Works closely with client enterprise architects, platform teams, security and risk stakeholders, product owners and delivery teams to align solution designs with target-state architecture and roadmaps.
- Understands the client’s engineering practices, delivery lifecycle and maturity; guides teams to adopt appropriate development, testing, release, documentation, operational-readiness and governance practices.
- Provides hands-on technical leadership throughout delivery, including architecture decisions, design assurance, technical troubleshooting, and transition of solutions into production support.
- Communicates complex technical choices clearly to both senior business stakeholders and engineering teams, articulating trade-offs, dependencies, risks and recommended decisions.