Machine Learning Systems Engineer

Chair.com.pk; Toronto; Hybrid; CAD 110,000 - 170,000; Full time

Job summary

Quix is seeking a Machine Learning Systems Engineer to design and operate ML pipelines, deployment infrastructure, and monitoring for production-ready models.

You will emphasize data quality, pipeline stability, and efficient inference while collaborating with data engineers and stakeholders to ensure reliability and observability across the ML lifecycle.

This role focuses on reproducible, auditable systems and clear handoffs to production teams, enabling scalable ML at enterprise scale.

Qualifications

• Production ML experience is essential for reliability and operability.
• Proficiency with ML frameworks such as TensorFlow, PyTorch, scikit-learn, XGBoost.
• Experience with MLOps tools (MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI).
• Understanding data quality, pipeline reliability, and feature consistency for production ML.

Responsibilities

• 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.

Skills

Production ML

ML frameworks

MLOps tools

Data quality

Kubernetes

Communication

Tools

MLflow

Kubeflow

SageMaker

Azure ML

Vertex AI

Kafka Streams

Job description

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.