AI and Machine Learning integrated into existing web and software systems
The focus is on applied implementations that are production-ready, maintainable, and aligned with established engineering practices.
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AI Scoping and Technical Assessment
Clear direction before implementation.
Support for defining scope, feasibility, and delivery approach for AI and ML initiatives. Work is grounded in technical constraints, data readiness, and integration requirements.
- Technical feasibility assessment
- AI capability scoping
- Delivery planning and prioritisation
AI and ML System Integration
Embedding AI into live platforms.
Integration of AI and ML components into existing applications and services using API-driven and service-oriented approaches.
- Model integration into applications
- System and workflow integration
- Scalable inference setup
Deep Learning Engineering
Neural network-based models for complex data problems.
Design and implementation of deep learning models where advanced pattern recognition or representation learning is required.
- Model architecture design
- Training and evaluation
- Performance optimisation
AI Chatbot Development
Conversational systems integrated into digital platforms.
Development of AI-driven chat and conversational interfaces designed to integrate with existing applications, services, and data sources.
- Conversational flow design
- Backend and API integration
- Deployment within web applications
AI Ops and ML Ops
Operational management of AI systems.
Implementation of processes and tooling to support deployment, monitoring, versioning, and reliability of AI and ML models in production.
- Model deployment pipelines
- Monitoring and performance tracking
- Lifecycle and version management
Natural Language Model Engineering
Language-focused AI models.
Development of NLP models for text analysis, classification, and language-based application features.
- Text processing and feature extraction
- Model training and validation
- Integration into applications
AI System Support and Maintenance
Ongoing support for deployed AI systems.
Support for monitoring, optimisation, and incremental improvement of AI and ML components in live environments.
- Performance monitoring
- Issue resolution
- Model updates and enhancements
Working With Engineering Teams
- Delivery-focused collaboration.
- Senior, production-focused engineers
- Clear technical communication
- Experience with modern web and software stacks
Work is delivered as part of existing teams and workflows, with a focus on predictable outcomes and long-term maintainability.