Senior Machine Learning Engineer - NLP + LLM

A Senior Machine Learning Engineer is a skilled professional who excels in designing, developing, and implementing end-to-end solutions for machine learning problems. They handle more intricate projects independently and possess an understanding of algorithms, programming languages like Python, and relevant libraries/frameworks. This role involves collaborating with teams, contributing to model architecture, optimizing performance, and staying current with advancements in the field to create effective machine-learning solutions. Responsibilities Design, develop, and deploy machine learning models and algorithms that solve complex business problems and deliver actionable insights. Evaluate and compare various approaches for solving NLP challenges, with a deep understanding of their respective strengths and limitations. Demonstrate expertise in large language models (LLMs), understand their capabilities, limitations, and potential enhancement techniques such as agent-based methodologies and tool integration, and apply strategies to overcome their inherent constraints. Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to understand project requirements and develop machine learning solutions. Clean, preprocess, and analyze large-scale structured and unstructured data sets to extract meaningful features and patterns. Evaluate and validate models using appropriate evaluation metrics, and iteratively improve model performance through experimentation and optimization techniques. Work closely with software engineers to integrate machine learning models into production systems, ensuring scalability, reliability, and efficiency. Stay up to date with the latest advancements in machine learning, deep learning, and related technologies, and share knowledge with the team. Mentor junior machine learning engineers, fostering their professional growth and knowledge development. Follow secure development, testing, and deployment guidelines and practices to adhere to the overall security of the system under consideration. Requirements B.E..Techm.TechPhD candidates' entries with significant prior experience in the fields above will be considered. Background and knowledge of recent advances in machine learning, deep learning, natural language processing, and LLMs with at least 3 years of professional work experience working on real-world data. Hands-on experience with machine learning libraries, sklearn, PyTorch, and TensorFlow. Hands-on experience working with LLMs for complex problems. Excellent mathematical skills and background, significance tests, and advanced probability concepts. Ability to perform both independent and collaborative research. Excellent written and verbal communication skills. A proven ability to work in a cross-discipline environment in defined time frames. Knowledge and experience in deploying large-scale systems using distributed and cloud-based systems is a big plus. Hands-on experience in systems engineering is a big plus.