Software Engineer, Backend (AI Infrastructure)
Summary
Build and scale MLOps infrastructure for AI/ML pipelines, including CI/CD, model training/inference, and monitoring to improve AI model deployment velocity.
In this role, you will be responsible for driving the execution of crucial infrastructure and platform initiatives related to AI/ML pipelines. These pipelines are designed for highly efficient and scalable model Training, & Inference. The responsibilities include building and developing tools, automation of redundant tasks, and CI/CD systems. This role requires someone with a strong collaborative and growth mindset. You will also look after the career development of the engineering team members.
What You'll Do:
- Design, develop and deploy MLOps infrastructure for to improve the velocity of development and deployment of AI and deep learning models: CI/CD, input data unit and statistical testing, experiment tracking, model registry, and monitoring and alerting of production features
- Design, develop and deploy scalable cloud AI/ML pipelines for customer-facing inference and visualization services
- Develop, implement and administer best MLOps practices relating to model development automation, evaluation, testing and deployment
- Focus on addressing availability issues, work on scaling pipelines, and improving features while maintaining SLAs on performance, reliability, and system availability
- Work with technical leads and managers to understand project requirements and business needs and collaborate with engineers across teams to identify and deliver features, services & pipelines
- Collaborate with cross-functional teams such as Backend, Frontend, and Platform to ensure the development and delivery of end-to-end product features, and robust and scalable AI pipelines
- Mentor junior team members
What We're Looking For:
- Bachelors Degree in Computer Science, Electrical Engineering, or related field.
- 1-3+ years experience in designing, implementing, and operating scalable software systems and services
- Hands-on experience with MLOps tools (e.g., Docker, Kubernetes, Kubeflow, Spark, Airflow, AWS, CI/CD)
- Solid CS foundations including in data structures, algorithms and software engineering
- Excellent verbal and written communication skills.
- You collaborate effectively with other teams and communicate clearly about your work.
- Knowledge of one or more of computer vision, deep learning, machine learning, or statistical and predictive modeling is a strong plus