MLOps DevOps Engineer

We are seeking a skilled MLOps DevOps Engineer to join our AI team. In this role, you will be responsible for building and maintaining the infrastructure required to deploy, scale, and manage machine learning models. You will work closely with data scientists and ML engineers to streamline the ML lifecycle, from experimentation to production. Key responsibilities include designing CI/CD pipelines for ML models, managing Kubernetes clusters for model serving, implementing monitoring and logging for ML systems, and ensuring the reliability and scalability of our AI platform. You will also contribute to automating model training and evaluation workflows, and collaborate on developing best practices for MLOps. The ideal candidate has hands-on experience with cloud platforms (AWS, GCP, or Azure), containerization technologies (Docker, Kubernetes), and ML tools like Kubeflow, MLflow, or TFX. Strong scripting skills in Python or Go are essential, as is a solid understanding of software engineering principles. We offer a competitive salary, equity, and the chance to work on cutting-edge AI technologies in a fast-paced environment. As a key member of our engineering team, you will play a critical role in bridging the gap between model development and production deployment. Your work will directly impact the speed and reliability of our AI-powered products, enabling us to deliver value to our customers faster. You will have the opportunity to work on challenging problems related to model serving, feature stores, and data pipelines. We value collaboration and continuous learning, and we encourage you to share your knowledge with the team. If you are passionate about DevOps and excited about the future of AI, we would love to hear from you.

• 3+ years of experience in DevOps or MLOps roles.
• Proficiency with cloud platforms (AWS, GCP, or Azure).
• Strong experience with Docker and Kubernetes.
• Experience with CI/CD tools like Jenkins, GitLab CI, or GitHub Actions.
• Familiarity with ML frameworks and tools such as TensorFlow, PyTorch, Kubeflow, or MLflow.
• Excellent scripting skills in Python, Bash, or Go.
• Strong problem-solving and communication skills.
• Ability to work in a fast-paced, agile environment.