The MLOps Engineer is responsible for designing, building, and maintaining the end-to-end machine learning lifecycle, from development to production. You'll bridge the gap between data science and engineering to ensure our models are deployed reliably and at scale.
What You’ll Be Doing
- Automate and orchestrate ML pipelines for training, testing, and deployment.
- Implement CI/CD practices specific to machine learning workflows.
- Monitor and manage production ML models for performance, drift, and accuracy.
- Collaborate with data scientists to transition models from research to production-ready systems.
- Ensure scalability, reliability, and security of the ML infrastructure.
What We’d Love To See
- 3–5 years of experience in MLOps, DevOps, or a similar role.
- Strong expertise in containerization (Docker, Kubernetes) and cloud platforms (AWS, Azure, GCP).
- Proficiency in scripting languages like Python and knowledge of ML frameworks (TensorFlow, PyTorch).
- Experience with MLOps tools like MLflow, Kubeflow, or Sagemaker.
- Solid understanding of CI/CD tools such as Jenkins, GitLab, or GitHub Actions.
It’d Be Great If You Had
- Experience with monitoring tools like Prometheus and Grafana.
- Knowledge of big data technologies like Spark or Hadoop.
