Senior MLOps Engineer

Factor Funding Company

. Build and automate CI/CD pipelines for machine learning models and data workflows using GitHub Actions, GitLab CI, or Jenkins.

Posted 10/31/2026full-timeRemoteSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in MLOps with a focus on deploying and scaling machine learning pipelines, utilizing advanced tools and technologies for CI/CD, containerization, and cloud infrastructure management. Proficient in collaborating with cross-functional teams to ensure seamless integration and operational efficiency of ML models.

Highest-signal resume keywords

MLOps ExperienceCI/CD Pipeline DevelopmentContainerization with DockerCloud Platform Proficiency (AWS, GCP, Azure)Infrastructure as Code (Terraform)

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonBash ScriptingMachine Learning Lifecycle ManagementVersioning Tools (MLflow, DVC, Kubeflow)Monitoring Solutions (Prometheus, Grafana, Arize)

Soft Skills

Strong Communication SkillsCollaboration

Tools & Technologies

GitHub ActionsGitLab CIJenkinsKubernetes (EKS, GKE, AKS)

Industry Keywords

Machine LearningMicroservicesData WorkflowsOperational HealthData Drift

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformGrafanaJenkinsKubernetesMicroservicesPrometheusPythonTerraform

About the role

Key responsibilities & impact

Build and automate CI/CD pipelines for machine learning models and data workflows using GitHub Actions, GitLab CI, or Jenkins.
Architect and manage containerized microservices and orchestration platforms for ML workloads using Docker and Kubernetes (EKS, GKE, AKS).
Implement and manage ML lifecycle, tracking, and versioning tools such as MLflow, DVC, or Kubeflow.
Provision, configure, and maintain ML cloud infrastructure using Infrastructure as Code tools like Terraform.
Establish monitoring, alerting, and logging systems for deployed models using Prometheus, Grafana, or Arize to track data drift, model performance, and operational health.
Collaborate with data scientists and software engineers to transition ML models from experimental scripts into secure, low-latency, scalable production APIs.
Design, build, and maintain infrastructure, pipelines, and tools enabling seamless machine learning deployment and operation across enterprise environments.
Own ML workflows end-to-end, from automated training and versioning to production monitoring and continuous integration.

Requirements

What you’ll need

5+ years of hands-on experience in MLOps, with a proven track record of deploying and scaling ML pipelines in enterprise environments.
Advanced English proficiency (written and spoken) with strong communication skills to collaborate effectively across multidisciplinary teams.
Strong Python and Bash scripting skills, with expertise in containerization (Docker) and orchestration frameworks (Kubernetes).
Deep hands-on experience with cloud platforms (AWS, GCP, Azure), Infrastructure as Code (Terraform), and CI/CD pipelines.
Demonstrated experience with dedicated MLOps platforms (MLflow, DVC, Kubeflow) and model observability/monitoring solutions (Prometheus, Grafana, Arize).

Benefits

Comp & perks

Ownership through equity participation.
Annual company retreat.
Education bonus for continuous learning.
Company-wide winter break.
Paid time off.
Optional in-person events and meetups.
Tailored career roadmaps.
High-performance culture.