Backend & API Engineer

Job Purpose

The Backend & API Architect will be responsible for designing and governing the backend architecture, APIs, integration frameworks and technology services that underpin the organisation's AI and digital ecosystem.

The role will define scalable, secure, resilient and high-performance backend architectures capable of supporting AI applications, intelligent automation, data-intensive workloads, digital products and enterprise platforms. The Architect will establish API standards, integration patterns, microservices architecture and technology principles while ensuring seamless integration between AI models, applications, enterprise systems and external platforms.

The role will work closely with AI/ML engineers, data architects, software engineers, DevOps, cybersecurity, product teams and business stakeholders to translate business and AI use cases into robust production-grade technology solutions.

Key Responsibilities

A. Backend Architecture

• Define and maintain the target backend architecture for AI and digital platforms.

• Design scalable, modular and resilient backend systems using modern architectural patterns.

• Establish appropriate use of microservices, event-driven architecture, serverless technologies and distributed systems.

• Design backend platforms capable of supporting high-volume, low-latency AI workloads.

• Ensure architectures support scalability, availability, maintainability and fault tolerance.

• Lead architectural decisions relating to application services, middleware, databases, messaging and integration platforms.

• Develop architecture blueprints, technical standards, reference architectures and design patterns.

B. API Architecture & Management

• Define enterprise API architecture, standards and governance frameworks.

• Design secure, reusable and scalable RESTful, GraphQL and event-driven APIs where appropriate.

• Establish API lifecycle management standards covering design, development, testing, deployment, versioning, monitoring and retirement.

• Develop API strategies that enable interoperability between AI solutions, enterprise applications and third-party platforms.

• Define authentication, authorization, throttling, rate limiting and API security standards.

• Promote API-first and service-oriented architecture principles.

• Ensure APIs are well documented, discoverable and reusable across technology teams.

C. AI & Machine Learning Integration

• Architect backend services that integrate AI/ML models into enterprise applications and customer-facing solutions.

• Design APIs and services for model inference, orchestration, prompt management and AI-agent interactions.

• Support integration with Large Language Models (LLMs), machine learning platforms and AI services.

• Architect Retrieval-Augmented Generation (RAG), vector search and knowledge-retrieval services where applicable.

• Design reliable model-serving and inference architectures capable of supporting production workloads.

• Establish patterns for integrating AI services with enterprise data, workflows and business applications.

• Collaborate with AI/ML teams on model deployment, scalability, observability and lifecycle management.

D. Integration Architecture

• Design integration architectures connecting AI platforms with core enterprise systems.

• Define appropriate integration patterns for synchronous and asynchronous communication.

• Architect event-driven and message-based integrations using technologies such as Kafka or equivalent platforms.

• Ensure interoperability across cloud, on-premise and hybrid environments.

• Assess and recommend integration technologies based on scalability, security, cost and business requirements.

• Reduce point-to-point integrations through reusable services and enterprise integration patterns.

E. Cloud & Distributed Systems

• Define backend architecture across cloud and hybrid environments.

• Work with cloud engineering teams to optimise architectures on platforms such as AWS, Azure or Google Cloud.

• Ensure appropriate use of containers, Kubernetes, serverless services and managed cloud technologies.

• Design for horizontal scalability, disaster recovery, business continuity and geographic resilience.

• Establish architecture principles for cloud-native AI applications.

• Evaluate emerging technologies and determine their applicability to the organisation's technology landscape.

F. Security & Risk

• Embed security-by-design principles throughout backend and API architecture.

• Design secure identity, access management and service-to-service authentication mechanisms.

• Ensure compliance with applicable data protection, cybersecurity and technology regulations.

• Work with cybersecurity teams to address API vulnerabilities, data exposure and application security risks.

• Incorporate encryption, secrets management, auditability and secure coding principles into architecture designs.

• Ensure AI integrations appropriately protect confidential, customer and enterprise data.

G. Performance, Reliability & Observability

• Design systems for high availability, resilience and predictable performance.

• Establish standards for logging, monitoring, tracing and application observability.

• Define service-level objectives and appropriate performance benchmarks.

• Identify and resolve architectural bottlenecks and scalability constraints.

• Design mechanisms for graceful degradation, failover and recovery.

• Promote proactive performance testing and capacity planning.

H. Technical Governance

• Provide architectural oversight across backend and API development initiatives.

• Review and approve solution architecture and technical designs.

• Establish technology standards, frameworks and reusable architecture components.

• Conduct architecture reviews and ensure compliance with approved principles.

• Maintain architecture decision records and technical documentation.

• Identify technical debt and develop remediation strategies.

• Provide technical leadership and mentorship to software engineering and architecture teams.

I. Engineering & Delivery

• Partner with engineering teams throughout the software development lifecycle.

• Translate architecture into actionable technical requirements and implementation roadmaps.

• Support Agile, DevOps and CI/CD practices.

• Ensure architecture decisions are practical, implementable and aligned with delivery priorities.

• Promote automation in testing, deployment, infrastructure and application management.

• Work with Product Managers and business teams to balance functionality, time-to-market, scalability and technical quality.

Qualifications & Experience

• Bachelor's degree in Computer Science, Software Engineering, Information Technology, Computer Engineering or a related discipline.

• At least 5 years of experience in software engineering, backend development, solution architecture or enterprise architecture.

• Significant experience designing large-scale backend and API platforms.

• Proven experience with microservices, distributed systems and cloud-native architectures.

• Strong experience with API management, integration architecture and enterprise application integration.

• Practical experience integrating AI/ML capabilities into enterprise or digital applications.

• Experience with LLMs, RAG, vector databases, AI agents or model-serving architectures is highly desirable.

• Strong experience with at least one major cloud platform such as AWS, Azure or Google Cloud.

• Experience with containerisation and orchestration technologies, particularly Docker and Kubernetes.

• Strong knowledge of databases, caching, messaging and event-streaming technologies.

• Experience with CI/CD, DevOps and infrastructure automation.