Job Description

About the Role

We are seeking an experienced MLOps Engineer to join our team and drive the operationalization of machine learning models at scale. In this role, you will work closely with data scientists, ML engineers, and software developers to streamline model deployment, automate workflows, and ensure the reliability and performance of AI solutions in production. This position is completely remote and open to both C2C and W2.

Responsibilities

• Design, build, and maintain ML pipelines , ensuring efficient deployment, monitoring, and scaling of models.
• Implement CI/CD pipelines for machine learning workflows.
• Automate model versioning, testing, and deployment using tools like MLflow, Kubeflow, or SageMaker .
• Optimize model performance, reliability, and resource utilization in cloud and on-prem environments .
• Manage infrastructure-as-code (IaC) solutions using Terraform, CloudFormation, or Kubernetes .
• Ensure data integrity, reproducibility, and governance for ML models in production.
• Implement model monitoring, logging, and alerting for drift detection and performance degradation.
• Collaborate with data engineering teams to integrate machine learning pipelines with data systems.
• Work with security teams to ensure ML systems are compliant with security and privacy best practices.

Required Qualifications

• 5+ years of experience in MLOps, DevOps, or ML Engineering roles.
• Strong programming skills in Python, Bash, or Go .
• Hands-on experience with cloud platforms (AWS, GCP, or Azure) and ML services like SageMaker, Vertex AI, or Azure ML .
• Proficiency in containerization (Docker, Kubernetes) and orchestration tools.
• Experience with CI/CD pipelines (GitHub Actions, Jenkins, ArgoCD, or GitLab CI/CD).
• Knowledge of monitoring and logging tools (Prometheus, Grafana, ELK, or OpenTelemetry).
• Familiarity with feature stores, model registries, and metadata tracking (Feast, MLflow, or Kubeflow).
• Strong understanding of ML lifecycle, model drift, and retraining strategies .
• Experience with IaC tools (Terraform, Helm) and version control (Git).
• Excellent problem-solving skills and ability to work in fast-paced, cross-functional teams .

Preferred Qualifications

• Experience in serverless architectures (AWS Lambda, GCP Cloud Functions).
• Exposure to real-time ML inference systems .
• Familiarity with LLM deployment and fine-tuning .
• Knowledge of compliance standards like GDPR, SOC 2, or HIPAA for AI models.

Why Join Us?

• Work on cutting-edge ML deployment challenges.
• Competitive salary, stock options, and comprehensive benefits.
• Learning and growth opportunities in AI, DevOps, and Cloud .
• Collaborative, innovative, and inclusive work culture.

About us
At Core Defender AI , we are committed to building scalable, secure, and innovative solutions that transform the way businesses operate. As part of our team, you’ll work with cutting-edge technologies and contribute to the development of systems that redefine the industry.

 

Job Tags

Remote job,

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