Technology Leader · Enterprise Architect · AI & Cloud Transformation
AI & Data Transformation Leader | Enterprise Architect
I design and lead enterprise technology transformations across AI, data, cloud and modern application platforms — connecting business strategy with scalable architecture and measurable outcomes. For the past decade I have led each generation of a global life-sciences enterprise’s R&D data-to-AI backbone, from golden master data to today’s generative and agentic AI portfolio.
Technology Leadership
Six capability areas, each grounded in delivered programmes — not aspiration.
Career Journey
Twenty years at a global IT services and consulting firm — from portal architect to Enterprise Architect in the AI & Data practice, with a decade of embedded leadership at a global life-sciences enterprise. Select a role to expand.
Business context: industrialise generative AI for R&D scientists — beyond pilots, to governed and adopted platforms that accelerate drug discovery.
Leadership: primary stakeholder interface for the platform portfolio; plans, budgets, risks and multi-team execution across client and vendor teams.
Architecture: translate business requirements into scalable specifications across a GenAI research assistant (chat, deep-research and document-reasoning modes), an AI/ML platform with end-to-end lineage, an agentic lab-in-the-loop hypothesis-generation system, and a knowledge-graph platform.
Technology: Azure OpenAI, Azure AI Studio, Vertex AI (Gemini), Databricks, MLflow, GraphRAG, Kong, BigQuery, Snowflake.
Outcome: AI capability embedded in scientists’ daily workflows across literature review, biomarker discovery and model deployment. [METRIC NEEDED — adoption & cycle-time gains]
Business context: unify fragmented scientific and enterprise data estates onto governed cloud platforms serving data scientists worldwide.
Delivery: a GCP-based scientific data & ML platform (BigQuery, GKE, Dataproc, Dataflow) and an Azure-based enterprise data platform (Databricks, Synapse, ADLS, Snowflake), run as a dual-cloud pair; led a major data-pipeline migration off a legacy system.
Architecture: established a development data fabric on data-mesh and domain-driven-design principles — data as a product: discoverable, interoperable, secure, trustworthy.
Outcome: self-serve platforms powering AI/ML modelling and analytics across business units; 20–30% operational efficiency gains.
Business context: transform an IT-system-centric estate into a governed data economy with FAIR principles and marketplace capability.
Delivery: built and ran the product group behind an MDM platform (golden data for clinical studies, compounds and substances, consumed by 50+ systems), a reference-data platform, a data catalog & lineage tool, an ontology platform, a data-quality tool, a data-virtualisation fabric, and DataOps services on Kafka, Kong and REST/GraphQL.
Innovation: an enterprise voice Q&A assistant and an AI-powered scientific lab automation initiative — recognised as a top-seeded innovation from 50,000+ global entries in an internal innovation programme.
Outcome: data accuracy up 40%; golden data adopted as the enterprise single source; the foundation for every subsequent AI initiative.
Scope: horizontal consulting role across life-sciences and banking clients — AI & data strategy, technology assessments, roadmaps, business cases and ROI models; architecture of secure, scalable AI & data platforms; emerging-technology advisory to CIOs/CTOs.
Outcome: programmes delivering £5–10M savings each and 30% faster time-to-market.
Scope: group-wide strategic review programme — customer identity & access management, enterprise portal consolidation, bulk-payment capture with straight-through processing.
Outcome: strategic online-channel capabilities delivered on time and within budget across multi-vendor teams.
Technical Manager, a global consumer-electronics manufacturer (2013) · Business Process Architect, a major aerospace manufacturer (2012) · Transformation Lead, a Danish telecom operator (2012) · Transformation Lead, an agricultural insurance provider in India (2010–11) · Team Lead, CRM for a major US automotive manufacturer (2008–09) · Technical Architect, enterprise architecture group — a large enterprise portal programme (2005–11).
Selected Transformations
Every case follows the same discipline: challenge → strategy → architecture → leadership → scale → outcome. All are from delivered work.
Challenge: move R&D AI beyond pilots in a regulated environment. Strategy: a governed platform portfolio, not point solutions. Architecture: GenAI research assistant, AI/ML platform with lineage, agentic lab-in-the-loop research system, knowledge-graph infrastructure. Leadership: programme lead across client & vendor teams. Scale: enterprise R&D, Azure + GCP. Outcome: AI in scientists’ daily workflows. [METRIC NEEDED]
Challenge: fragmented scientific data estate. Strategy: two governed platforms — GCP for scientific ML, Azure for enterprise data. Architecture: lakehouse (Databricks, Synapse, BigQuery, Snowflake), streaming, self-serve workspaces. Leadership: platform programme direction, major legacy migration. Scale: global business units. Outcome: 20–30% efficiency gains; AI/ML powered enterprise-wide.
Challenge: data locked in systems; slow, inconsistent access. Strategy: data-as-a-product on data-mesh principles. Architecture: data-virtualisation fabric with base/derived/unified views, domain-driven design, catalog-backed discovery. Leadership: product-group accountability. Scale: organisation-wide real-time access. Outcome: standardised, trusted data for every consumer.
Challenge: no single source of truth for core scientific entities. Strategy: master & reference data as enterprise products. Architecture: an MDM platform paired with a reference-data platform, built to FAIR-aligned standards. Leadership: built and ran the delivering product group. Scale: 50+ consuming systems. Outcome: data accuracy up 40%; golden copy across business processes.
Challenge: ungoverned metadata, inconsistent quality, unfindable data. Strategy: five-product capability: catalog, MDM/RDM, ontology, data quality, knowledge centre. Architecture: a catalog & lineage tool, a data-quality tool, an ontology platform and a data-modelling tool over lakes & warehouses. Leadership: product-group leadership with governance & stewardship communities. Scale: enterprise R&D. Outcome: findable, accessible, interoperable, reusable data — the AI foundation.
Challenge: manual experiment capture; knowledge trapped in documents. Strategy: instrument the lab; make enterprise knowledge conversational. Architecture: IoT + vision + speech capture; a data-harmonisation engine feeding a cloud voice-Q&A and conversational-AI stack. Leadership: innovation-programme lead. Scale: R&D innovation hub. Outcome: top-seeded innovation from 50,000+ global entries in an internal innovation programme; drastic waste reduction in experiment documentation.
Challenge: fragmented transaction-banking systems. Strategy: consolidate & decommission; strategic online channel. Architecture: CIAM, enterprise portal aggregation, straight-through-processing bulk payments. Leadership: delivery manager, multi-vendor. Scale: group strategic-review programme. Outcome: competitive, streamlined offering delivered on time, on budget.
Challenge: prove agentic-AI architecture end-to-end, at production quality. Strategy: build every layer personally. Architecture: multi-provider LLM gateway, agentic runtime with HITL, hybrid RAG (vector search + BM25), knowledge graph, GxP/ALCOA+ auditability, policy-as-code, orchestrated CI/CD, Kubernetes fleet. Scale: 65+ API modules, 230+ data models, 160+ UI pages. Outcome: current, hands-on command of the stack — implemented, not conceptual.
Architecture Showcase
Selected patterns I work with. Marked Delivered where drawn from shipped programmes, Reference where conceptual.
AI & Technology Vision
Adding AI features to existing systems is the transitional state. The end state is AI-native: platforms designed around agents, retrieval and human-AI workflows from the first architectural decision. I build and operate this pattern today.
Agents will take structured work across the SDLC and business processes; people move to intent, judgement and exception handling. The operating model — HITL gates, accountability, skills — is the hard part, and it is a leadership problem, not a tooling one.
In regulated industries, auditability is not friction — it is the licence to deploy. Audit-by-design, append-only history and policy-as-code let AI move fast because the controls are structural, not procedural.
Every AI outcome is downstream of data quality. Golden data, ontologies and knowledge graphs — once “governance projects” — are now the retrieval substrate of enterprise AI. The decade I spent on FAIR data is why the AI chapter works.
Agentic SDLC — agents for requirements, code, test, deployment paired with engineers — changes team shape and economics. Leaders should pilot it inside a governed delivery flow, measure, then scale what proves out.
Reusable platform capability — gateway, retrieval, evaluation, observability — compounds; one-off AI projects don’t. Enterprise AI investment should build the platform once and let every use case ride it.
Thought Leadership
Published internally as AI Lead of the R&D Innovation Hub; widely circulated across the organisation. [INFORMATION REQUIRED — shareable public version?]
Seminars for enterprise clients on long-term data strategy and cognitive data solutions that influenced client technology-investment decisions.
Articles on agentic-AI architecture, AI governance in GxP environments, and data-to-AI transformation. [Placeholder — no published articles yet]
Credentials
Education: B.Tech (Information Technology), a technical university in India, 2005.
Technology Map
Not everything is an equal skill — this is deliberately levelled.
Leadership Philosophy
“Business first. Every architecture decision I make is traceable to a business case, a risk reduced, or a measurable outcome — the £5–10M savings per programme exist because the technology was never the point.”
I build reusable capability — golden data, data fabric, LLM gateways — that every subsequent initiative rides. A decade at a global life-sciences enterprise shows the compounding.
Security, auditability and data integrity engineered in structurally — inherited, not remembered. That is how AI ships in regulated industries.
I still build. Leaders who understand the retrieval layer and the audit trail make better investment decisions — and earn engineering teams’ trust.
Consolidation and decommissioning are first-class workstreams in every programme I run — from banking transformation to life-sciences application rationalisation.
KPI regimes at portfolio, programme and team level; prioritisation by scored business value, criticality, risk and cost — visible to everyone.
Recruiting and mentoring engineers into high-performing, collaborative teams — 250+ practitioners across the current portfolio.
Open to senior technology leadership and architecture conversations — IT Director, Technology Director, Enterprise/Chief/Principal Architect, and AI transformation roles.
Stevenage, United Kingdom