About Zeeshan
I help regulated enterprises deploy, secure, and govern AI systems that deliver real business value — without exposing your organisation to compliance failures, security vulnerabilities, or regulatory risk.
As an independent AI & Cloud consultant, I bring hands-on expertise across AI governance, cybersecurity architecture, and agentic AI systems, with a track record delivering into financial services and public sector environments. Typical engagements include AI security framework design, GenAI adoption strategy, multi-cloud architecture (Azure & GCP), MLOps governance, and enterprise agentic platform builds.
Where I add distinct value is bridging the gap between advanced AI capability and the governance, risk, and compliance demands of regulated industries — translating complex technical delivery into outcomes your leadership team can stand behind.
English
Native or bilingual
Can work on-site
Manchester (up to 50km)
Experience
- Co-Operative Bank,Infrastructure Design & Governance Manager (AI Security & Cloud)May 2025 - Today (1 year and 1 month)Manchester, UK• • Serve as primary cybersecurity liaison between Infrastructure Design, AI Engineering teams, and enterprise cyber security function for IBM watsonx.ai and Azure OpenAI Service deployments, ensuring all AI applications and data products meet security standards before presentation to CTO and board-level audiences• • Built and documented comprehensive cybersecurity processes for AI platform operations, establishing security review workflows, threat modelling procedures, and secure development guidelines covering model deployment, data handling, and API security across hybrid cloud infrastructure
- Adaptec AI,Gen AI Security ConsultantMarch 2025 - December 2025 (9 months)Boots UK Pharmacy, Dubai, United Arab Emirates• • Created and presented AI transformation strategy and business case to airport executive leadership and board of directors, securing AI adoption program through ROI modelling, risk analysis, and stakeholder alignment across operational divisions• • Led change management and communication planning for AI transformation initiative, developing stakeholder engagement frameworks, resistance mitigation strategies, and adoption metrics that drove organizational transition from legacy operations to AI-enabled airport platform• • Architected enterprise AI infrastructure leveraging Microsoft Azure ecosystem, Azure OpenAI Service for passenger assistance chatbot, Power Platform for operations dashboards, Microsoft Fabric for unified data platform, and Microsoft Purview for AI governance and DLP controls across GenAI workloads• • Led end-to-end product lifecycle for Agentic AI and enterprise automation initiatives, from opportunity identification and strategy definition through to prototyping, pilot deployment and scaling across regulated environments• • Designed and built multi-agent AI systems and autonomous workflow orchestration using Azure AI Foundry and Claude (Anthropic), replacing and augmenting manual processes across compliance-driven organisations• • Architected RAG frameworks, vector-backed knowledge intelligence platforms and LLM pipelines as production-grade AI products, working hands-on with engineering and data science teams to ensure scalable, reliable delivery• • Defined governance frameworks, guardrails and risk controls for enterprise AI products, aligned to NIST AI RMF, EU AI Act and ISO/IEC 42001, covering full model lifecycle traceability, auditability and explainability• • Engaged C-suite audiences including CTOs, cybersecurity leads and data governance owners to align agentic AI product strategy with regulatory requirements and operational workflows, driving cross-functional adoption
- Abexsun Technology,Gen AI ConsultantOctober 2024 - January 2025 (3 months)United Kingdom• • Developed multi-agent AI systems using Google Vertex AI Agents, LangGraph and CrewAI, driving intelligent workflow automation and enterprise decision-making at scale• • Architected and deployed scalable MLOps pipelines on GCP integrating Compute Engine, Cloud Run and GKE, ensuring performant and governed AI model delivery across production environments• • Built cloud-based data engineering pipelines using BigQuery, Cloud Storage and Cloud Pub/Sub, integrating structured and unstructured data sources for real-time AI inference and analytics workloads• • Implemented CI/CD automation using Google Cloud Build, Jenkins and GitHub Actions for continuous deployment of AI/ML models and microservices• • Enforced cloud security and AI governance best practices across IAM, Cloud Identity and enterprise security policies including data encryption and compliance controls• • Translated complex AI and cloud architecture decisions into clear business outcomes for both technical and non-technical senior stakeholders
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Education
- Generative AI & LLMs Architecture & Data PreparationIBMGenerative AI & LLMs Architecture & Data Preparation
- Generative AI for Cybersecurity ProfessionalsIBMGenerative AI for Cybersecurity Professionals