Principal Systems Engineer- Enterprise AI (Design & Implementation)

Role Summary

Our client is hiring a highly technical Systems Engineer to join its North American R&D and solutioning hub.

This role is designed for an engineer who can move fluently between systems thinking, high-level architecture and hands-on software engineering. The successful candidate will design and build scalable, enterprise-grade AI systems, reusable software development kits, application frameworks, libraries, reference architectures and solution templates that can be adapted across multiple enterprise use cases.

You will not be responsible for designing isolated features or single-purpose applications. You will engineer complete systems by understanding business requirements, performance expectations, user volumes, integration constraints, tenancy models, security requirements and operational behaviour.

The ideal candidate has a proven track record of designing and delivering complex enterprise solutions while remaining close to the code.

What Will You Build

You will contribute to the design and development of:

• Enterprise-grade artificial intelligence platforms and solutions
• Multi-tenant software architectures
• Reusable SDKs, libraries and engineering frameworks
• Application templates and reference implementations
• AI-enabled enterprise applications
• Modular product components that can be assembled into broader solutions
• Integration layers connecting existing platforms, services and applications
• Scalable backend services and distributed systems
• High-level and detailed system architecture artefacts
• Proofs of concept that can be industrialised into production-grade products

KEY RESPONSIBILITIES

SYSTEMS DESIGN & ARCHITECTURE

• Own the high-level design of scalable, secure and maintainable enterprise systems
• Translate complex business and technical requirements into clearly defined system architectures
• Design systems based on expected user volumes, performance requirements, data throughput, concurrency, availability and operational constraints
• Define system boundaries, component responsibilities, service interactions, data flows, interfaces and integration patterns
• Design architectures capable of supporting a minimum of 1,000 users while maintaining reliability, performance and scalability
• Evaluate architectural trade-offs across performance, cost, maintainability, security and speed of delivery
• Develop reference architectures and reusable design patterns for enterprise AI solutions
• Create architecture documentation, technical specifications, system diagrams and design decision records

ENTERPRISE AI SOLUTION ENGINEERING

• Design enterprise AI solutions that combine models, SDKs, applications, APIs, data platforms and existing enterprise systems
• Determine how existing technologies and applications can be integrated and orchestrated into a unified solution
• Architect systems supporting AI inference, intelligent automation, agentic workflows, retrieval, data processing and enterprise integrations
• Design reusable AI solution components rather than one-off implementations
• Ensure AI systems are production-ready, observable, secure and capable of operating at enterprise scale
• Collaborate with AI, machine learning, product and platform teams to convert experimental capabilities into reliable software products

HANDS-ON SOFTWARE ENGINEERING

• Remain directly involved in coding, prototyping and technical implementation
• Build backend services, APIs, libraries, SDKs, system components and reusable engineering tools
• Produce clean, efficient, tested and maintainable production code
• Prototype complex architectural concepts to validate feasibility, performance and integration behaviour
• Review code and guide engineering teams on system-level implementation decisions
• Diagnose performance bottlenecks, architectural limitations and system-level failures
• Work across multiple programming languages and choose technologies based on system requirements rather than personal preference