Role and Responsibilities

• Leverage distributed training systems to build scalable machine learning pipelines for model training and deployments in IT/OT Products space.
• Design and implement solutions to optimize distributed training execution in terms of model hyperparameter optimization, model training/inference latency and system-level bottlenecks.
• Research and implement state of the art LLM models for different business use cases including finetuning and serving the LLMs.
• Ensure ML Model performance, uptime, and scale, maintaining high standards of code quality and thoughtful design quality and monitoring.
• Optimize integration between popular machine learning libraries and cloud ML and data processing frameworks.
• Build Deep Learning models and algorithms with optimal parallelism and performance on CPUs/ GPUs.

 

Qualifications

• A Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or a related field is typically required.
• Proficiency in programming languages such as Python, R, or Java is essential.
• Familiarity with machine learning libraries (e.g., TensorFlow, Scikit-learn, PyTorch) and data processing tools (e.g., Apache Spark, Hadoop) is also required.
• Strong analytical and problem-solving skills
• Knowledge of machine learning algorithms
• Experience with distributed computing systems, such as Hadoop and Spark, and cloud platforms (e.g., AWS, Azure, or Google Cloud) is beneficial.
• Effective communication with team members, stakeholders, and clients is essential for success in this role.