DevOps Engineer - MLOps

About the Role:

We are seeking a highly skilled and experienced DevOps Engineer to join our dynamic team. The ideal candidate will have a solid background in MLOps to support and scale our development pipelines and machine learning infrastructure. You will work at the intersection of development and operations to automate processes, manage cloud infrastructure, and ensure high system uptime and security.

Key Responsibilities:

• CI/CD Management: Design, build, and maintain automated Continuous Integration and Continuous Delivery (CI/CD) pipelines to fast-track code releases.

• Infrastructure as Code (IaC): Provision and manage cloud environments using automation tools like Terraform, Ansible, or Cloud Formation.

• Containerization: Deploy and run scalable containerized applications using Docker and Kubernetes.

• Cloud Management: Maintain secure and scalable infrastructure on cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP).

• Monitoring & Logging: Set up system observability frameworks using tools like Prometheus, Grafana, or the ELK stack to track health, set alerts, and diagnose errors.

• Collaboration: Act as a bridge between software developers and IT operations to streamline workflows and resolve production issues.

• DevSecOps Implementation: Implement DevSecOps practices to embed security across the entire software development lifecycle.

• Security Scanning: Integrate automated security scanning and vulnerability checks into CI/CD pipelines.

• Secret Management: Manage sensitive data and secrets securely using Azure Key Vault or AWS Secrets Manager.

• Compliance & Best Practices: Ensure infrastructure compliance and enforce security best practices across all cloud deployments.

Required Skills and Qualifications:

• Experience: Minimum 4 years of experience in DevOps or Site Reliability Engineering.

• Proficiency with Git version control and collaborative workflows.

• Hands-on experience with CI/CD tools like Jenkins, GitLab CI/CD, CircleCI, etc.

• Experience with cloud platforms (AWS/GCP/Azure), preferably AWS.

• Strong understanding of Docker, Kubernetes, and container orchestration.

• Working knowledge of MLOps tools and workflows (e.g., MLflow, DVC, Kubeflow, SageMaker).

• Scripting in Python, Bash, or similar languages.

• Familiarity with Infrastructure as Code (IaC) tools.

• Experience integrating and deploying ML models in production environments.

• Experience with model monitoring and drift detection.

• Problem-solving Skills: Strong analytical and problem-solving skills.

• Communication Skills: Excellent communication and teamwork skills.

• Education: Bachelor's degree in Computer Science, Information Technology, or a related field is preferred.