Senior MLOps Engineer

KData AI; United States; Remote; CAD 110,000 - 180,000; Full time

Job summary

KData AI is seeking a Senior MLOps Engineer to lead the industrialization and orchestration of the full ML lifecycle. You will design, build, and maintain scalable ML pipelines that support deployment, monitoring, drift management, and governance across production systems.

You will bridge Data Science, Data Engineering, and DevOps, leveraging Snowflake, dbt, Terraform, and GitOps practices to ensure reproducible features, secure access, and robust CI/CD pipelines in a remote, Canada-based setup.

Qualifications

• 5+ years of hands-on experience in MLOps, Data Engineering, or DevOps.
• Proven experience with Snowflake ecosystem for ML workloads.
• Automation of CI/CD pipelines and infrastructure via IaC tools.
• Experience with dbt data transformations and data quality checks.
• Solid ML observability and drift detection experience.
• Strong governance, versioning, and RBAC practices.

Responsibilities

• Architect end-to-end MLOps solutions for deployment, scaling and retraining pipelines.
• Establish model governance and lineage with metadata tracking.
• Build model monitoring for performance, drift, and alerts.
• Implement centralized Feature Stores for reproducible features.
• Use IaC and GitOps to automate deployments and security controls.
• Integrate dbt with ML observability to ensure data/model integrity.

Skills

Snowflake ML

ML Monitoring

Data Transformation

Infra as Code

RBAC Governance

Tools

dbt

Terraform

Jenkins

GitOps

Job description

We are seeking a highly skilled and experienced Senior MLOps Engineer to lead the industrialization and orchestration of our complete machine learning lifecycle. In this role, you will design, build, and maintain robust, scalable ML pipelines, ensuring seamless model deployment, continuous monitoring, drift management, reproducibility, lineage, and governance. You will play a pivotal role in bridging the gap between Data Science, Data Engineering, and DevOps using modern cloud data architecture.

Key Responsibilities

• ML Lifecycle Industrialization: Architect and implement end-to-end MLOps solutions to automate model deployment, scaling, and retraining pipelines across production environments.
• Model Governance & Lineage: Establish comprehensive ML governance, tracking model lineage, versioning, metadata, and data quality across datasets and models.
• Monitoring & Drift Management: Build robust model monitoring solutions to track performance, data drift, and concept drift, triggering proactive alerts and automated retraining pipelines.
• Feature Engineering & Management: Implement and manage centralized Feature Stores to ensure reproducible features and consistent feature serving for training and inference.
• Infrastructure & CI/CD: Leverage Infrastructure as Code (IaC) and modern GitOps/CI/CD practices to automate deployments, ensure reproducibility, and enforce strict security/access controls.
• Data Transformation & Observability: Integrate data transformation tools (dbt) with data quality and ML observability frameworks to maintain end-to-end data and model integrity.

This is a remote position.

We are seeking a highly skilled and experienced Senior MLOps Engineer to lead the industrialization and orchestration of our complete machine learning lifecycle. In this role, you will design, build, and maintain robust, scalable ML pipelines, ensuring seamless model deployment, continuous monitoring, drift management, reproducibility, lineage, and governance. You will play a pivotal role in bridging the gap between Data Science, Data Engineering, and DevOps using modern cloud data architecture.

Key Responsibilities

• ML Lifecycle Industrialization: Architect and implement end-to-end MLOps solutions to automate model deployment, scaling, and retraining pipelines across production environments.
• Model Governance & Lineage: Establish comprehensive ML governance, tracking model lineage, versioning, metadata, and data quality across datasets and models.
• Monitoring & Drift Management: Build robust model monitoring solutions to track performance, data drift, and concept drift, triggering proactive alerts and automated retraining pipelines.
• Feature Engineering & Management: Implement and manage centralized Feature Stores to ensure reproducible features and consistent feature serving for training and inference.
• Infrastructure & CI/CD: Leverage Infrastructure as Code (IaC) and modern GitOps/CI/CD practices to automate deployments, ensure reproducibility, and enforce strict security/access controls.
• Data Transformation & Observability: Integrate data transformation tools (dbt) with data quality and ML observability frameworks to maintain end-to-end data and model integrity.

Requirements

Key Skills & Tech Stack

• Snowflake ML Ecosystem: Snowpark ML, Snowflake Model Registry, Feature Store, Snowflake RBAC.
• ML Lifecycle & Monitoring: Experiment Tracking, Model Monitor, ML Lineage, Drift Management, Data Quality & ML Observability.
• Data Engineering & Transformations: dbt (data build tool).
• Infrastructure & DevOps: Terraform, Jenkins, CI/CD, GitOps practices.
• Governance & Security: Role-Based Access Control (RBAC), data governance, lineage tracking.

Qualifications & Experience

• Experience: 5+ years of hands-on experience in MLOps, Data Engineering, or DevOps, with a strong track record of industrializing machine learning models into production.
• Hands-on Expertise: Proven experience using the Snowflake ecosystem (Snowpark, Snowflake Model Registry, Feature Store) for ML workloads.
• Automation & IaC: Proficiency in building CI/CD pipelines (Jenkins) and managing infrastructure via Terraform.
• Data Engineering: Deep experience with dbt for data transformation and establishing data quality checks.
• ML Observability: Solid background in implementing experiment tracking, model performance monitoring, and drift detection frameworks.
• Best Practices: Strong mastery of GitOps practices, reproducibility, version control, and granular access control (RBAC).