Senior Software Engineer – AI Infrastructure

Team: IT

Location: Canada

Commitment: Full-time

Workplace Type: remote

Accountabilities

• AI Infrastructure Development and Architecture

  • Design, develop, and maintain the infrastructure layer that powers AI agent systems in production environments.

  • Build high-performance backend services using Rust to support model inference, agent orchestration, and execution workflows.

  • Architect scalable distributed systems capable of handling high request volumes and supporting millions of users.

  • Establish foundational infrastructure components and reusable system primitives that enable AI agents to interact reliably with internal services.

  • Contribute to critical architecture decisions, balancing performance, reliability, scalability, and operational cost.

  • Machine Learning Infrastructure and MLOps

    • Develop and maintain reliable infrastructure for model serving, deployment, evaluation, and production monitoring.

    • Implement operational patterns that support consistent model delivery, performance evaluation, and continuous improvement.

    • Collaborate with AI and applied engineering teams to move experimental prototypes into robust, production-ready systems.

    • Optimize model inference and orchestration workflows to improve latency, throughput, resource utilization, and cost efficiency.

    • Support the integration of AI capabilities into production systems while maintaining predictable and reliable execution.

    • Reliability, Observability, and Performance

      • Design and implement monitoring, logging, tracing, and observability mechanisms to provide visibility into system behavior.

      • Establish guardrails, fault-handling strategies, and recovery mechanisms for agent-driven workflows and distributed services.

      • Identify and resolve performance bottlenecks, operational failures, and reliability issues in high-throughput production environments.

      • Apply reliability engineering principles to minimize service disruptions and maintain consistent performance under real-world workloads.

      • Ensure system correctness, resilience, and operational stability in environments with demanding latency and availability requirements.

      • Cross-Functional Collaboration and Technical Leadership

        • Partner with agent systems engineers, machine learning specialists, and infrastructure teams to define and deliver production-ready solutions.

        • Translate experimental requirements into scalable architectures and maintainable engineering implementations.

        • Contribute to technical design discussions, infrastructure standards, and engineering best practices.

        • Take ownership of critical production systems, proactively identifying risks and driving improvements throughout the software lifecycle.

        • Support the evolution of the AI infrastructure platform as product requirements, workloads, and business needs grow.

        Requirements

        • At least five years of experience building, deploying, and operating high-scale production software systems.

        • Strong proficiency in Rust and systems-level programming, with the ability to develop efficient, reliable, and maintainable services.

        • Deep understanding of distributed systems, including service communication, concurrency, fault tolerance, and reliability engineering.

        • Proven experience optimizing system performance, particularly latency, throughput, resource utilization, and operational efficiency.

        • Experience operating services that support millions of users or handle high-throughput workloads in production.

        • Familiarity with machine learning infrastructure, model serving, or MLOps practices in production environments.

        • Experience designing and implementing observability solutions, including monitoring, logging, tracing, alerting, and failure recovery.

        • Strong understanding of production operations, incident investigation, system resilience, and performance troubleshooting.

        • Ability to collaborate effectively across infrastructure, applied engineering, and AI-focused teams to deliver complex technical solutions.

        • Strong ownership and accountability, with the ability to make sound technical decisions and manage critical systems in high-stakes environments.

        • Excellent analytical and problem-solving skills, with a systematic approach to diagnosing failures and improving system behavior.

        • Clear technical communication skills and the ability to contribute to architectural discussions and cross-functional engineering initiatives.

        Nice to have:

        • Experience building infrastructure for AI agents, autonomous workflows, or large language model (LLM)-powered applications.

        • Background in high-performance networking, asynchronous systems, or low-latency architectures.

        • Experience with container orchestration, cloud-native infrastructure, and modern deployment platforms.

        • Familiarity with model evaluation frameworks, inference benchmarking, and monitoring model performance at scale.

        • Experience building new platforms or foundational systems in fast-moving environments, particularly during early-stage development and the transition from prototype to production.

        Benefits

        • Employment type: Full-time position.

        • Work arrangement: Fully remote, with Brazil included among the eligible hiring locations.

        • Competitive compensation: Salary and compensation details are not specified in the original job description.

        • High-impact engineering: Opportunity to build foundational AI infrastructure supporting intelligent systems at significant scale.

        • Technical ownership: Direct involvement in architectural decisions, infrastructure design, and production engineering.

        • Advanced technology environment: Hands-on work with Rust, distributed systems, AI agent infrastructure, model serving, and performance optimization.

        • Cross-functional collaboration: Work alongside specialists in AI, machine learning, agent systems, and infrastructure engineering.

        • Professional development: Exposure to emerging AI infrastructure practices, production MLOps, and complex distributed systems challenges.

        • Engineering excellence: An environment that emphasizes reliability, observability, scalability, operational correctness, and continuous improvement.

        • Global collaboration: Opportunities to work with distributed engineering teams across multiple regions.