Location : Remote

Work Timings : Arabian Standard Time (GMT+3)

About the Role :

We are looking for a skilled and proactive MLOps Engineer with 5 to 8 years of experience to support our machine learning lifecycle operations. The ideal candidate will have a solid DevOps mindset, hands-on experience with modern MLOps tools, and a passion for automating and optimizing ML pipelines in cloud environments. This is a remote role, operating on Arabian Standard Time (GMT+3).

Key Responsibilities :

- Design, implement, and maintain CI/CD pipelines for ML models using tools like Jenkins, GitHub Actions, or GitLab CI.

- Manage containerized environments using Docker and Kubernetes.

- Deploy, manage, and track machine learning models using MLflow, SageMaker, or TFX.

- Set up monitoring tools (e.g., Prometheus, Grafana) for infrastructure and model performance, including model drift detection.

- Work with cloud DevOps tools and infrastructure on AWS, GCP, or Azure.

- Automate scripting and infrastructure tasks using Python and Bash.

- Collaborate with data scientists, engineers, and platform teams to ensure robust, scalable model operations.

Required Skills & Qualifications :

Technical Skills :

- Proven experience with CI/CD tools : Jenkins, GitHub Actions, GitLab CI.

- Strong knowledge of Docker and Kubernetes for container orchestration.

- Hands-on experience in model deployment frameworks : MLflow, SageMaker, or TFX.

- Familiarity with monitoring tools such as Prometheus, Grafana, and model drift detection techniques.

- Experience with cloud platforms and DevOps tools on AWS, GCP, or Azure.

- Proficient in scripting languages like Python and Bash.

Soft Skills :

- Strong DevOps mindset and passion for automation and reliability.

- Excellent team collaboration and communication skills.

- Ability to work independently and deliver under minimal supervision.