AI/ML Ops Engineer

Canada
Remote
Posted Aug 17, 2026
1 position

$131,000–$164,250 / year

Opens an external site

Employment type: Full-time
Experience level: Senior · 5+ years
Posting language: English
Working hours: 40 hours per week
Location requirements: Country, Canada

Job summary

You will own the operational backbone of the AI/ML capability, managing the full lifecycle from model training to production deployment. This includes building automated pipelines, ensuring high availability, and implementing robust monitoring and governance for all AI/ML solutions.

Job details

Blackpoint Cyber is the leading provider of world-class cybersecurity threat hunting, detection and remediation technology. Founded by former National Security Agency (NSA) cyber operations experts who applied their learnings to bring national security-grade technology solutions to commercial customers around the world, Blackpoint Cyber is in hyper-growth mode, fueled by a recent $190m series C round. ROLE SUMMARY As AI/ML Ops Engineer, you will own the operational backbone of Blackpoint's AI/ML capability — taking models from training through production deployment and owning the full AI/ML loop across every pipeline the team runs at scale. You will be instrumental in building out this new function: standing up deployment pipelines, taking trained scripts and putting them on endpoints that can accept live requests, and ensuring every solution you ship is production-grade, highly available, and well monitored. As the team's scope expands, you will take on ownership of testing across the board and automate training, monitoring, and deployment pipelines across the AI lifecycle. You will report to the Vice President of AI and Data and work closely with Engineering, the Security Operations Center (SOC), and the Adversary Pursuit Group (APG). WHO YOU ARE 5+ years of hands-on ML Engineering experience, including having personally trained and deployed models into a production environment — you know what it takes to take an AI product from prototype to live service. A well-architected mindset — built for efficiency, performance, security, and reliability, with genuine comfort owning deployment pipelines end-to-end. Strong analytical and problem-solving abilities, with a focus on data-driven decision-making. Excellent communication and interpersonal skills, with the ability to influence and collaborate with stakeholders at all levels. WHAT YOU'LL BRING Experienced in: Cloud-based ML Infrastructure (AWS) Model Development, Evaluation, & Deployment Operations (SageMaker, Bedrock) Inference Streams & Event-Driven Processing (Kafka, Spark) MLOps Workflows (MLFlow, Sagemaker Pipelines) Infrastructure as Code & Pipeline Automation (Terraform, AI CI/CD, GitHub Actions) ML Governance (Data, Model, & Feature Versioning, Monitoring & Testing) Containerized Services (Docker, Kubernetes, ECS/EKS) Scripting Languages (Python, Bash) Query Languages (SQL, SparkSQL) GitFlow, CI/CD workflows & DevOps best practices Experience with AI-Assisted development life cycle Building high-availability, production-grade systems with strong visibility and alerting baked in from day one Nice to Have: Transformer Neural Networks Agile Scrum/Kanban Anthropic, OpenAI, LiteLLM APIs and SDKs Experience in Cybersecurity, IoT, or NLP fields Grafana or CloudWatch (observability tooling) HOW YOU'LL MAKE AN IMPACT Own the AI/ML loop end-to-end at scale — across all pipelines, from model training through deployment, monitoring, and retirement. Develop, optimize, and deploy ML models, drawing on direct, hands-on experience training models yourself. Design, build, and administer model-building and serving infrastructure, taking trained scripts and standing them up as live endpoints that can accept real-time requests. Implement ML workflows as containerized Infrastructure as Code, using Terraform, GitHub Actions, Docker, and Kubernetes. Build and automate standardized container pipelines for training, feature engineering, and inference channels, with CI/CD managed through GitHub. Own test strategy across the full ML pipeline — model validation, integration, load, and deployment testing — as the team's testing scope continues to expand. Build visibility and alerting into every deployed pipeline and hold all AI/ML solutions to a production-grade, highly-available, well-monitored bar. Develop ML governance utilities for oversight and administration of deployed infrastructure. Implement data, feature, and model lifecycle best practices. Contribute to AI architecture and design decisions, taking primary ownership of ML pipeline work. Collaborate closely with cross-functional teams, including Engineering, Blackpoint Cyber's Security Operations Center (SOC), and the Adversary Pursuit Group (APG). Blackpoint Cyber welcomes and encourages applications from qualified individuals of all races, colors, religions, sex, sexual orientation, gender identity or expression, national origin, age, marital status, or any other legally protected status. We are committed to equality of opportunity in all aspects of employment. For eligible employees in the US, Blackpoint offers competitive Health, Vision, Dental, and Life Insurance plans, a robust 401k plan, Discretionary Time Off, and other minor perks. International employees receive competitive benefits in accordance with local market standards and applicable country requirements. Blackpoint believes all employees should share in the company’s success – equity participation is available to employees globally, with program details varying by location and employment structure.

What you’ll do

Requirements

The role requires 5+ years of hands-on ML Engineering experience with a proven track record of deploying models into production environments. Candidates must possess strong expertise in cloud-based ML infrastructure, containerization, and automated deployment pipelines.