Description
MLOps Engineer
Location: Calgary
Department: Artificial Intelligence
Location Type: HYBRID
Employment Type: FULL_TIME
Role Overview
The MLOps Engineer at Precision AI is responsible for operationalizing machine learning systems end-to-end, from model packaging and deployment to monitoring, reliability, and lifecycle management in production.
This role focuses on building robust, automated ML infrastructure that enables our AI teams to deploy, version, monitor, and continuously improve models used in real-world agricultural operations. You’ll work closely with AI/ML researchers, robotics engineers, and embedded systems teams to ensure models move smoothly from experimentation into reliable production systems.
While this is a core MLOps role, Precision AI operates in a physical, edge-deployed environment (UAVs and field hardware), so some responsibilities extend beyond typical cloud-only MLOps work.
This role is hybrid out of our Calgary office due to hands-on system integration and testing requirements.
Key Responsibilities
ML Platform & Deployment
- Design, build, and maintain automated pipelines for model packaging, validation, and deployment
- Operationalize ML models for production APIs and services
- Support deployment targets across cloud, on-prem, and edge environments
- Implement CI/CD workflows for ML systems, including automated testing and release processes
- Manage model promotion across environments (dev, staging, production)
Reliability, Monitoring & Governance
- Build and maintain model monitoring for performance, latency, failures, and drift
- Implement logging, alerting, and observability using tools such as CloudWatch or equivalent
- Manage model versioning, metadata, and registries
- Ensure reproducibility and auditability across datasets, training runs, and deployments
- Define and enforce MLOps best practices across teams
Data & Pipeline Management
- Support data ingestion, validation, and dataset versioning workflows
- Ensure training and evaluation datasets are properly registered and traceable
- Collaborate with ML teams to improve data quality, lineage, and lifecycle management
AI and Computer Vision Expertise
- Work effectively with common computer vision tasks such as image classification, object detection, segmentation, and tracking.
- Understand model training principles, including data preprocessing, augmentation, loss functions, evaluation metrics, and overfitting/underfitting trade-offs.
- Collaborate with ML researchers and engineers to translate model requirements into production-ready systems.
Edge & Performance-Aware Operations
- Support deployment of ML models to resource-constrained environments, including UAV-based systems
- Assist with optimizing and compiling AI models for edge devices (e.g., Jetson Orin) and mobile platforms, focusing on latency, throughput, and memory efficiency.
- Collaborate with engineering teams on operational considerations for edge inference
Relevant Experience
- 3+ years of experience in MLOps, ML platform engineering, or production ML systems
- Experience deploying and operating ML models in production environments
- Strong background in Python and ML tooling ecosystems
- Hands-on experience with containerization and orchestration (e.g., Docker, Kubernetes)
- Familiarity with AWS services for deployment, monitoring, and infrastructure
- Experience implementing testing, monitoring, and alerting for ML systems
- Experience building or supporting scalable data pipelines
What You Bring
- Strong understanding of MLOps principles: automation, reliability, observability, and reproducibility
- Experience bridging ML research and production engineering
- Comfort working cross-functionally with ML, software, and systems teams
- Pragmatic mindset focused on operational stability and continuous improvement
- Ability to operate in environments where software meets physical systems
Bonus:
- Experience with UAVs or other autonomous systems.
- Background in agricultural technology or edge AI applications.
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