At a Glance
- Tasks: Build and operate ML pipelines, ensuring reliable production use of machine learning models.
- Company: Join Quix, a forward-thinking tech company focused on ML innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on operational reliability and career advancement.
- Why this job: Make a real impact by bringing cutting-edge ML solutions to life in enterprise environments.
- Qualifications: Experience in building ML systems and proficiency with ML frameworks like TensorFlow or PyTorch.
Build and operate the ML pipelines, model deployment infrastructure, and monitoring systems that bring machine learning into reliable production use. Quix is looking for a Machine Learning Systems Engineer who specializes in the engineering discipline required to take ML models from experimentation to production operation. This role is for someone who understands that ML reliability in enterprise environments depends on data quality, pipeline stability, inference efficiency, and systematic model monitoring — not just model performance metrics. Work is calm, technical, and delivery-focused. You'll help teams make durable decisions in enterprise environments where reliability and operational clarity matter.
What You'll Do
- Design and maintain ML training pipelines, feature engineering workflows, and data validation stages.
- Build model deployment infrastructure: packaging, versioning, serving, A/B testing frameworks, and rollback mechanisms.
- Implement model performance monitoring, data drift detection, and automated retraining triggers.
- Optimize inference serving for latency, throughput, and cost efficiency across batch and real-time use cases.
- Collaborate with data engineers to ensure feature stores and data pipelines meet ML pipeline requirements.
- Establish ML experiment tracking, model registry, and lineage management practices.
- Document ML system behavior, data dependencies, and operational requirements for production handoff.
What We're Looking For
- Strong production experience building and operating ML systems, not only research or experimentation.
- Proficiency with ML frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost, or equivalents.
- Experience with MLOps tooling: MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI, or equivalent.
- Deep understanding of data quality requirements, pipeline reliability, and feature consistency for production ML.
- Experience with containerized ML workloads and Kubernetes-based deployment environments.
- Ability to communicate ML system behavior and limitations clearly to non-ML stakeholders.
Nice to Have
- Experience with stream processing frameworks for real-time feature engineering: Flink, Spark Streaming, or Kafka Streams.
- Background in model security, adversarial testing, or ML-specific risk frameworks.
- Experience in regulated industries where model governance and audit trails are required.
Production ML systems are only as valuable as their operational reliability. This role ensures that models trained in experimentation reach production without degrading, continue to perform under distribution shift, and provide the monitoring visibility that engineering and business teams need to trust automated decision systems.
The role is pre-selected. Resume and mobile phone are required so the intake can support verification when connected.
Machine Learning Systems Engineer in Toronto employer: Chair.com.pk
Quix is an exceptional employer for a Machine Learning Systems Engineer, offering a calm and technical work environment that prioritizes reliability and operational clarity. With a strong focus on employee growth, Quix provides opportunities to collaborate with talented teams while working on cutting-edge ML technologies in a supportive culture that values innovation and continuous improvement. Located in a vibrant tech hub, employees benefit from access to industry-leading resources and a network of like-minded professionals, making it an ideal place for those seeking meaningful and rewarding employment.
