Remote opportunity at
LaterMLOps Engineer
Later, an influencer marketing company providing an AI-powered platform and data analytics for brand campaigns, is seeking an MLOps Engineer. In this remote position, the specialist…
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About This Role
Later, an influencer marketing company providing an AI-powered platform and data analytics for brand campaigns, is seeking an MLOps Engineer. In this remote position, the specialist will join the infrastructure team to help expand cloud environments, DevOps processes, and machine learning capabilities. The work involves partnering with platform engineers, data scientists, and product developers to build and maintain the systems that handle application delivery, data workflows, model experimentation, and machine…
Job Description
Later, an influencer marketing company providing an AI-powered platform and data analytics for brand campaigns, is seeking an MLOps Engineer. In this remote position, the specialist will join the infrastructure team to help expand cloud environments, DevOps processes, and machine learning capabilities. The work involves partnering with platform engineers, data scientists, and product developers to build and maintain the systems that handle application delivery, data workflows, model experimentation, and machine learning deployments.
This role is well-suited for a professional with a strong DevOps foundation who wants to gain deeper experience in cloud infrastructure, Kubernetes, GitOps, and MLOps. Daily responsibilities include maintaining reliable infrastructure, automating repeatable tasks, improving deployment workflows, and supporting the systems that empower engineering and data teams to operate efficiently and safely.
Responsibilities
- Develop and execute infrastructure roadmaps across DevOps and MLOps aligned with product and data growth plans
- Collaborate with engineering, data, and machine learning groups to ensure scalability, reliability, security, and automation
- Evaluate and integrate tools that enhance system efficiency, observability, developer experience, and model deployment
- Build and maintain infrastructure for deploying machine learning models into production via CI_CD pipelines, Flask-based APIs, and orchestration tools
- Automate training pipelines, model registries, validation, deployments, and rollbacks utilizing Amazon SageMaker and Postman
- Establish monitoring systems for tracking model performance, latency, data drift, and resource usage with Amazon CloudWatch, Prometheus, and Grafana
- Manage training and inference infrastructure across AWS and GCP, including BigQuery datasets and GPU workloads
- Utilize Terraform to provision and govern cloud infrastructure through infrastructure-as-code practices
Requirements
- Two to five years of hands-on DevOps or cloud engineering experience within production settings
- Demonstrated expertise with Kubernetes, EKS, Helm, and microservices
- One to two years of working experience with AWS and SageMaker
- One to two years of background building data pipelines
- One to two years of experience handling large language model deployments on Kubernetes
- One to two years of experience managing Kubernetes clusters utilizing GPU nodes
- Proven track record applying Terraform for scalable and auditable infrastructure-as-code
Core Skills
Benefits
- Eligibility to participate in various benefits plans offered to permanent team members
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
Is this a remote position?
Yes, this role is open to fully remote candidates.
What is the employment type?
The posting specifies that this is a full-time position.
What salary does the position offer?
The salary range listed is between 120,000 and 140,000 CAD.
What primary technical skills are required?
Candidates must have 2-5 years of DevOps experience, expertise in Kubernetes and EKS, and 1-2 years of experience with AWS, SageMaker, data pipelines, LLM deployments, and GPU-enabled Kubernetes nodes.
Sample Interview Questions
AI-generated questions tailored to this specific role — a preview of the full practice set.