Senior AI & Technology Architecture Specialist

About Invent Consulting Limited Invent Consulting Limited is a technology consulting firm specialising in data analytics, enterprise software solutions, and digital transformation. We work with organisations across multiple sectors to deliver tailored, context-specific technology solutions that improve efficiency, enable innovation, and drive measurable business outcomes.

About the Role

Invent Consulting Limited is seeking a highly experienced Senior AI & Technology Architecture Specialist to provide technical advisory, architecture review and engineering enablement support for a client engagement. The specialist will operate as a deep technical advisor and reviewer across three key domains: AI Architecture & Economics, API/Integration Engineering, and AI-enabled Engineering Productivity.

The role will provide the governance team with sufficient technical depth to challenge teams, identify gaps and drive improvement. The assignment requires a strong understanding of emerging AI technologies, enterprise architecture, API and integration engineering, software engineering practices and the practical application of AI across engineering and technology environments.

Key Responsibilities

A. AI Models, Agentic AI & Technology Landscape Assist in establishing an AI model evaluation framework — benchmarking models including OpenAI, Anthropic, Google, open-source and specialist models against accuracy, reasoning, latency, security, scalability and cost. Define model-selection standards and determine, using evidence, which models should be used for coding, agents, document processing, analytics, customer-facing workloads and other enterprise use cases, including model routing.

Compare capabilities and selection criteria across Azure, AWS, Meta and other relevant models and platforms. Optimise AI economics and provide guidance on evaluating token consumption, inference costs, hosting costs and ROI, including when smaller or open-source models may be preferable to frontier models. Develop Agentic AI architecture and define patterns for agents, tools, memory, orchestration, MCP/APIs, guardrails, observability and human-in-the-loop controls.

Maintain an emerging-AI technology radar, continuously assessing new models, frameworks and capabilities and translating them into actionable enterprise recommendations. Review Generative AI opportunities including Copilot, ChatGPT, Claude and other AI solutions. Review multistep AI solutions and automation platforms such as n8n, Power Automate, Automation Anywhere, Make, Zapier and Camunda, as well as RAG systems and associated cost and token-consumption considerations.

B. API, Integration & Platform Engineering Establish an API-ready architecture standard to ensure modernised platforms expose business capabilities through secure, documented and reusable APIs before production cutover. Conduct technical reviews of API and integration enablement capabilities, including API gateways and custom integration hubs.

Introduce API readiness gates covering API coverage, integration testing, documentation, performance and security as architecture and go-live criteria. Diagnose integration delivery bottlenecks and determine whether delays result from platform limitations, architecture, skills or talent, tooling, governance, engineering practices or adoption of best practices.

Standardise API engineering practices, including API-first design, REST/event-driven patterns, versioning, authentication, API gateways, observability and lifecycle management. Identify opportunities to increase reusable enterprise integration capability through reusable APIs, connectors, integration services and common patterns.

C. Engineering Efficiency & AI-enabled Software Delivery Work with the client to baseline engineering productivity, including lead time, deployment frequency, failure rates, recovery time, developer effort and pipeline bottlenecks. Identify high-value AI automation opportunities across requirements, coding, testing, security, CI/CD, infrastructure, observability and incident management using AI agents or multistep solutions.

Support the deployment of controlled AI agents across engineering pipelines for code generation and review, test generation, vulnerability remediation, DevOps and operational support. Identify opportunities to overlay AI agents on engineering flows, with particular focus on quality, software and integration. Support the standardisation of engineering platforms and practices through common toolchains, golden paths, templates, reusable pipelines and engineering standards.

Measure engineering value realisation through improvements in delivery speed, quality, reliability, cost and developer productivity. Develop and support metrics that demonstrate adoption, effectiveness, maturity and measurable revenue or cost-saving outcomes.

Requirements

Qualifications & Experience Demonstrated senior-level experience in technology architecture, engineering advisory, enterprise technology or a closely related technical field. Strong practical experience in AI architecture, Generative AI and emerging AI technologies. Demonstrated understanding of AI model evaluation, model selection, AI economics and cost optimisation.

Experience designing or reviewing Agentic AI architectures, including agents, tools, memory, orchestration, APIs, guardrails and observability. Strong experience in API and integration architecture, including REST and/or event-driven architectures, API gateways, authentication, versioning and lifecycle management. Experience with enterprise integration platforms, reusable APIs, connectors and integration services.

Strong understanding of modern software engineering, DevOps, CI/CD, testing, security and engineering productivity practices. Experience identifying and implementing AI-enabled opportunities across software engineering and technology delivery. Experience conducting technical architecture reviews, assessing engineering capabilities, identifying gaps and providing evidence-based recommendations.

Ability to work effectively with technical teams and governance stakeholders at senior level. Experience translating emerging technologies into practical enterprise recommendations and measurable business value. Key Attributes Deep technical expertise with the ability to operate across AI, architecture, integration and software engineering.

Analytical and evidence-driven, with the ability to evaluate technologies objectively based on capability, performance, security, scalability and cost. Strong advisory mindset, with the confidence to challenge existing approaches and identify technical gaps constructively. Enterprise perspective, with an understanding of architecture standards, governance, security, scalability and technology economics.

Practical and delivery-focused, able to translate emerging AI capabilities into viable enterprise solutions. Strong problem-solving skills and the ability to diagnose complex technical and engineering challenges. Strong communication and stakeholder management skills, with the ability to communicate complex technical matters clearly to both technical and governance audiences.

Curious and forward-looking, with an interest in continuously assessing emerging AI models, frameworks, platforms and engineering practices. Results-oriented, with a focus on measurable improvements in delivery speed, quality, reliability, cost and productivity.