Principal Systems Engineer- Enterprise AI (Design & Implementation)

Flatgigs•Montreal, QC

About The Position

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.

Requirements

Strong professional experience in systems engineering, software architecture, platform engineering or backend engineering.
Demonstrated expertise in high-level system design and architectural thinking.
Proven experience designing and delivering at least five enterprise-grade solutions.
Each qualifying solution should have supported, or been designed to support, at least 1,000 users.
Strong hands-on software development experience with Python, Go, Rust (language agnostic)
Experience building scalable, distributed or service-oriented systems.
Experience designing multi-tenant platforms or enterprise SaaS products.
Strong understanding of system performance, capacity planning, concurrency and scalability.
Experience integrating multiple existing applications, platforms or services into a unified enterprise solution.
Experience designing APIs, SDKs, libraries, frameworks or reusable product components.
Experience converting prototypes or proofs of concept into production-grade systems.
Strong understanding of reliability, security, observability and maintainability.
Ability to produce clear high-level design documentation and architecture diagrams.

Responsibilities

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.
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.
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.
Design multi-tenant architectures with appropriate tenant isolation, access control, configuration and resource management.
Define strategies for shared and dedicated infrastructure across different enterprise customers.
Design systems that can scale horizontally and vertically based on workload patterns.
Understand the performance, capacity, limitations and ideal use cases of each system component.
Establish approaches for load distribution, caching, asynchronous processing, resilience and fault tolerance.
Design for high availability, graceful degradation, recoverability and operational continuity.
Anticipate future scale and prevent architectural decisions that create long-term platform limitations.
Work closely with CTOs, architects, product leaders, AI engineers and software development teams.
Lead technical discussions and communicate architectural decisions clearly to technical and non-technical stakeholders.
Challenge weak assumptions and identify risks before implementation begins.
Provide technical direction without becoming disconnected from implementation.