Professional summary
Senior Data & AI Engineer with deep expertise designing and deploying production-grade AI solutions across financial services,
retail, and public sector clients. Proven track record delivering end-to-end LLM applications, including semantic search, document
intelligence, and NLP pipelines, using both open-source models (LLaMA, Mistral, Qwen, Gemma, Phi-3) and commercial APIs (OpenAI,
Cohere). Experienced in bridging data science and software engineering within fast-paced consulting environments, translating
ambiguous business challenges into scalable, governed AI platforms on Azure, Databricks, and Microsoft Fabric.
Recognised technical leader with a history of mentoring engineers, leading architecture workshops, and supporting business
development conversations on AI and ML. Eligible and experienced working in environments requiring security clearance.
Core capabilities
AI & LLM Engineering
LLM deployment: Mistral, LLaMA, Qwen, DeepSeek, Gemma, Phi-3
LangChain, vector databases, agentic AI systems
RAG pipelines, semantic search, chatbot development
OpenAI & open-source model APIs
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Cloud & MLOps
Azure ML, Databricks, Microsoft Fabric, ADF
SageMaker, Vertex AI (GCP), Azure Data Lake
CI/CD pipelines, Terraform (IaC), MLOps principles
Event-driven architectures, CosmosDB, Service Bus
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Data Engineering & Platforms
Apache Spark, PySpark, Delta Lake, SSIS
Python (advanced), SQL, Bash scripting
Batch & streaming pipelines, data quality frameworks
Lakehouse architectures, data governance & lineage
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Consulting & Technical Leadership
Solution architecture & executive advisory
Team mentoring, Agile delivery, stakeholder management
Business requirements to AI solution translation
AI/ML business development conversations
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Professional experience
Lead architect and AI engineering consultant delivering cloud-native data platforms and LLM-powered analytics solutions for
public sector and commercial clients. Operating as a trusted technical advisor from discovery through to production deployment.
- Industrialised local LLMs (Mistral, Qwen, DeepSeek, LLaMA, Phi-3) using Apache Spark, enabling scalable document processing, semantic search, and NLP capabilities for enterprise clients.
- Designed and deployed end-to-end RAG pipelines integrating vector search with LLM inference layers, forming the basis of internal chatbot and content generation systems.
- Built automated PII anonymisation pipelines leveraging Microsoft Presidio integrated with Spark — demonstrating responsible AI and data governance in production environments.
- Architected Azure-native data platforms covering ingestion (ADF), transformation (Databricks/Spark), storage (ADLS, Delta Lake), and consumption layers — with full lineage and observability.
- Implemented MLOps-aligned deployment patterns: CI/CD pipelines, automated validation, logging and alerting for production-grade AI model reliability.
- Delivered high-volume data migrations and integration frameworks using SSIS, D365, and custom mapping pipelines across complex multi-system environments.
- Facilitated architecture workshops and produced executive-ready design artefacts, roadmaps, and AI governance models — directly supporting business development.
- Mentored junior engineers across Python, SQL, cloud engineering, and LLM tooling; established team coding standards and review processes.
Delivered analytical and engineering support for regulatory and digital transformation programmes within a major UK financial institution.
- Built automated Python and SQL pipelines for data quality, reconciliation, and regulatory reporting — reducing manual effort and improving accuracy.
- Conducted deep-dive analysis on commercial and operational datasets to support strategic stakeholder decision-making.
- Partnered with senior stakeholders to translate complex business requirements into scalable, auditable data solutions.
- Developed Spark-based batch pipelines with robust validation logic supporting credit card onboarding and fulfilment.
- Integrated APIs and internal systems to deliver high-quality, governed datasets for downstream operational use.
- Led global delivery teams on large-scale data migration and transformation programmes.
- Designed migration strategies ensuring accuracy, completeness, and operational continuity across distributed environments.
- Managed multi-supplier engineering teams with a focus on governance, quality, and stakeholder alignment.
Technical skills
| Languages |
Python (advanced), SQL, Bash |
| AI / LLM |
LangChain, OpenAI API, Cohere, Mistral, LLaMA, Qwen, DeepSeek, Gemma, Phi-3; RAG, vector databases, agentic AI |
| Cloud & ML |
Azure ML, Databricks, Microsoft Fabric, ADF, ADLS, CosmosDB; AWS SageMaker, GCP Vertex AI |
| Data Engineering |
Apache Spark, PySpark, Delta Lake, SSIS, Apache Kafka concepts, batch & streaming pipelines |