What you'll do:

• Design, build, and maintain scalable platforms that allow customers to upload, customize, and manage ML, DL, and LLM models across cloud and on-premises environments.
• Develop robust, scalable backend services and APIs, primarily using Python, to support ML operations.
• Build and maintain CI/CD pipelines to automate integration, testing, and deployment of backend services.
• Create solutions for efficient processing of large datasets and support machine learning models across distributed cloud environments.
• Implement unit and integration tests using frameworks like pytest and tox, ensuring code quality and reliability.
• Diagnose and resolve issues related to ML jobs and cloud infrastructure, primarily using Linux/Unix environments.
• Manage and deploy containerized applications using Docker and Kubernetes to streamline ML workflows.
• Work closely with data scientists and ML engineers to enhance the performance, reliability, and scalability of ML pipelines.
• Continuously explore and integrate emerging technologies to optimize backend systems and improve scalability and efficiency.
• Write efficient, scalable Python code, leveraging libraries such as PyTorch, scikit-learn, pandas, and numpy.

What makes you a great fit:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 6+ years of industry experience in software engineering with a focus on machine learning platforms.
• Proficiency in Python, with hands-on experience using libraries like PyTorch, scikit-learn, pandas, and numpy.
• Strong experience with containerization (e.g., Docker) and orchestration tools (e.g., Kubernetes).
• Solid understanding of CI/CD practices and tools.
• Experience with cloud platforms (AWS, GCP, Azure) and familiarity with serverless architecture.
• Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow) to streamline ML operations.
• Proven expertise in troubleshooting cloud infrastructure issues using Linux/Unix.
• Track record of developing scalable backend services and APIs.
• Knowledge of test-driven development (TDD) and experience with testing frameworks like pytest and tox.
• Excellent problem-solving skills with the ability to collaborate across cross-functional teams.
• Strong communication skills, with the ability to explain complex technical concepts to both technical and non-technical stakeholders.