What you’ll achieve
As an AI/ML Software Engineer, you will join a global team focused on leveraging AI/ML to drive innovation in our storage products and enhance efficiency across organizational processes.
You will:
Collaborate with architects, senior engineers, product managers and business stakeholders to understand complex problems in enterprise data storage products and engineering processes (e.g., testing, CI/CD) and identify opportunities for AI/ML solutions
Assist in the entire machine learning lifecycle, including data collection, cleaning, pre-processing, feature engineering, model training, evaluation, and deployment
Implement, optimize, and experiment with various AI and Machine Learning algorithms and models
Develop and maintain robust, scalable, and efficient AI/ML pipelines and infrastructure
Perform rigorous model testing, validation, and interpretability analysis to ensure accuracy, fairness, and reliability
Stay up-to-date with the latest advancements in AI, Machine Learning, and deep learning research and technologies
Document technical designs, code, and experimental results clearly and concisely
Contribute to a culture of continuous learning, innovation, knowledge sharing, and best practices within the team
Essential Requirements:
Technical Proficiency:
Solid theoretical understanding of core AI and Machine Learning concepts, including supervised, unsupervised, and reinforcement learning, LLMs, and common ML algorithms
Proficiency in major programming language used in AI/ML ( e.g., Python )
Experience with popular ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy)
Familiarity with data manipulation, analysis, and visualization techniques
Problem-Solving: Demonstrated ability to approach complex problems, break them down, and propose innovative AI/ML-driven solutions
Adaptability: Eagerness to learn new tools, technologies, and adapt to evolving project requirements in a fast-paced enterprise setting
Communication: Strong verbal and written communication skills to articulate technical concepts to both technical and non-technical audiences
Desirable Requirements:
Education: Bachelor's with 2 + year of experience or Master's degree with 2+ years of experience in Artificial Intelligence, Machine Learning, Computer Science, Data Science, or a closely related quantitative field with a strong emphasis on AI/ML
Experience in AI/ML in an enterprise or large-scale data environment
Experience with MLOps principles and tools (e.g., Docker, Kubernetes, CI/CD for ML)
Knowledge of distributed computing frameworks (e.g., Spark) for large datasets
Software development experience in C/C++
•Bachelor’s degree
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