Software engineering role focused on applied AI, internal platforms, and early stage technical decision making, working closely with stakeholders across the business.

I operate early in the problem lifecycle, partnering with technical and non technical teams to understand operational and data challenges, explore solution options, and translate business objectives into clear technical direction. A core part of my role is rapid experimentation and prototyping to assess feasibility, validate value, and inform longer term product and platform decisions.

Key areas of work include:

• Leading rapid proof of concept development to explore applied AI use cases, including agent based systems that translate natural language requests into structured data workflows and analytical pipelines

• Designing and delivering internal platforms and data pipelines, including CRM and analytics systems built from the ground up

• Shaping system architecture, data models, and integration patterns to ensure solutions remain scalable, maintainable, and adaptable as requirements evolve

• Building AI enabled systems that analyse unstructured data such as call transcripts and operational data to surface insights and support decision making

• Working closely with stakeholders to diagnose operational pain points, define requirements, assess trade offs, and align delivery with commercial and operational goals

Alongside delivery, I have introduced more structured ways of working around prototyping and early stage product development, ensuring clear documentation, ownership, and handover so that successful prototypes can move smoothly into beta and production phases.

I work across full stack engineering, data systems, and applied AI, and am comfortable balancing experimentation with production discipline. My focus is on building systems that support real workflows and decision making, rather than isolated features, and ensuring technology choices create long term leverage for the organisation.

Client facing engineering role focused on designing and delivering cloud native integrations and internal tooling for ecommerce and retail organisations.

I worked directly with senior client stakeholders, including business owners and operational leads, to understand complex operational constraints and translate commercial objectives into integration architecture and delivery plans. Much of the work involved diagnosing fragmented systems, data flows, and manual processes, then designing robust middleware solutions to connect SaaS platforms, third party APIs, and enterprise systems.

Key areas of work include:

• Partnering directly with client decision makers to analyse operational pain points, assess technical and commercial constraints, and present solution options with clear trade offs

• Designing and delivering bespoke cloud based integration platforms on GCP, acting as middleware between SaaS products, third party APIs, and complex ERP systems

• Leading the refactor and re platforming of legacy low code integrations into cloud native microservices, significantly reducing infrastructure costs while improving reliability, scalability, and long term maintainability

• Designing event driven data flows to support near real time stock visibility across in store and out of store channels

• Owning delivery end to end, including discovery workshops, technical design, implementation, deployment, and post release support

I regularly balanced delivery speed with architectural quality, making pragmatic decisions to meet client timelines while ensuring systems were resilient and extensible. The role required strong communication skills, comfort working in ambiguous problem spaces, and the ability to translate between business language and technical execution.

This experience developed my ability to operate in customer embedded environments, solve high impact integration problems, and deliver systems that directly support operational performance and commercial outcomes.

I worked closely with senior leadership and operations teams to analyse end to end business processes, identify failure points and inefficiencies, and design systems that materially improved operational capacity, data quality, and commercial outcomes. Much of the work focused on simplifying highly manual, error prone workflows and embedding automation directly into day to day operations.

Key areas of work include:

• Leading discovery and analysis of complex operational workflows within a regulated insurance context, mapping processes spanning multiple roles, systems, and data sources

• Designing and delivering automation that replaced spreadsheet driven calculations and manual document assembly, significantly reducing effort and error risk

• Building systems to handle complex pricing and hire calculations across multiple vehicle categories, time periods, and contractual rules, removing hundreds of manual calculation steps per case

• Automating the generation of core client deliverables by integrating structured data with document generation and evidence management workflows

• Working directly with directors and operational leaders to align system design with commercial objectives and regulatory constraints

These changes increased operational capacity by approximately twenty percent without additional headcount, directly improving revenue potential and consistency of output.

Alongside core workflow automation, I contributed to new product and data capabilities, including event driven services integrated with insurer systems to support earlier intervention and loss mitigation, and cloud based data pipelines that processed large volumes of unstructured historical documents into structured datasets for analysis and reuse.

This role strengthened my ability to diagnose real world operational problems, translate complex requirements into robust system design, and deliver technology that supports people, processes, and decision making at scale.