Software Engineer - AI/ML

Commvault is the gold standard in cyber resilience, empowering customers to recover from cyberattacks. They are seeking an Entry-Level Engineer to contribute to AI-driven features and services within their engineering organization, focusing on the AI Development Lifecycle.

Responsibilities:

• Participate in the full AI Development Lifecycle (ADLC): requirements analysis, data preparation, model development, evaluation, deployment, and monitoring
• Collaborate with software engineers, data scientists, and product managers to build AI-powered features into Commvault products
• Develop, test, and maintain scalable backend services and APIs that integrate machine learning models
• Assist in data preprocessing, feature engineering, and dataset management for model training and evaluation
• Implement model validation, performance tracking, and continuous improvement workflows
• Contribute to CI/CD pipelines for AI/ML systems, ensuring reliable deployment and versioning of models
• Write clean, maintainable, and well-documented code following engineering best practices
• Support debugging, performance tuning, and optimization of AI-enabled applications
• Stay current with emerging AI/ML technologies and apply relevant advancements where appropriate

Requirements:

• Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related field
• Strong programming skills in Python; familiarity with Java, C++, or Go is a plus
• Basic understanding of machine learning concepts (supervised/unsupervised learning, model evaluation, overfitting, etc.)
• Familiarity with common ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
• Understanding of software development fundamentals: data structures, algorithms, object-oriented design
• Exposure to REST APIs, microservices architecture, and cloud platforms (AWS, Azure, or GCP)
• Knowledge of version control systems (Git) and basic CI/CD concepts
• Strong problem-solving skills and attention to detail
• Internship or project experience involving AI/ML or data-driven applications
• Familiarity with MLOps practices (model deployment, monitoring, retraining pipelines)
• Experience working with large datasets and data processing tools (e.g., Pandas, Spark)
• Understanding of containerization technologies such as Docker and orchestration tools like Kubernetes
• Exposure to security, data privacy, or enterprise software systems