Director of Machine Learning

As a candidate for this role, you should have a Bachelor's or master's degree in computer science, mathematics, statistics, or a related field. You should possess 12+ years of experience in AI/ML, including 4+ years in a leadership role. It is essential to have end-to-end experience with scalable, resilient, and distributed machine learning systems. Proficiency in Python, GEN AI, ML, Natural Language Processing (NLP), Deep Learning (DL), and Graph Analytics is required. Experience in directly working with cloud providers such as AWS, Azure, and GCP would be a plus. Strong problem-solving skills and the ability to work collaboratively in a team environment are crucial for this position. Key Responsibilities: - Proficiency in operating machine learning solutions at scale, covering the end-to-end ML workflow. - Familiarity with real-world ML systems including configuration, data collection, data verification, feature extraction, resource and process management, analytics, training, serving, validation, experimentation, monitoring. - Obsession with service observability, instrumentation, monitoring, and alerting. Qualifications Required: - Bachelor's or master's degree in computer science, mathematics, statistics, or a related field. - 12+ years of experience in AI/ML, including 4+ years in a leadership role. - End-to-end experience with scalable, resilient, and distributed machine learning systems. - Proficient in Python, GEN AI, ML, Natural Language Processing (NLP), Deep Learning (DL), and Graph Analytics. - Experience in directly working with cloud providers such as AWS, Azure, and GCP is a plus. - Strong problem-solving skills and ability to work collaboratively in a team environment. - Proficiency in operating machine learning solutions at scale, covering the end-to-end ML workflow. - Familiarity with real-world ML systems (configuration, data collection, data verification, feature extraction, resource and process management, analytics, training, serving, validation, experimentation, monitoring). - Obsession with service observability, instrumentation, monitoring, and alerting. As a candidate for this role, you should have a Bachelor's or master's degree in computer science, mathematics, statistics, or a related field. You should possess 12+ years of experience in AI/ML, including 4+ years in a leadership role. It is essential to have end-to-end experience with scalable, resilient, and distributed machine learning systems. Proficiency in Python, GEN AI, ML, Natural Language Processing (NLP), Deep Learning (DL), and Graph Analytics is required. Experience in directly working with cloud providers such as AWS, Azure, and GCP would be a plus. Strong problem-solving skills and the ability to work collaboratively in a team environment are crucial for this position. Key Responsibilities: - Proficiency in operating machine learning solutions at scale, covering the end-to-end ML workflow. - Familiarity with real-world ML systems including configuration, data collection, data verification, feature extraction, resource and process management, analytics, training, serving, validation, experimentation, monitoring. - Obsession with service observability, instrumentation, monitoring, and alerting. Qualifications Required: - Bachelor's or master's degree in computer science, mathematics, statistics, or a related field. - 12+ years of experience in AI/ML, including 4+ years in a leadership role. - End-to-end experience with scalable, resilient, and distributed machine learning systems. - Proficient in Python, GEN AI, ML, Natural Language Processing (NLP), Deep Learning (DL), and Graph Analytics. - Experience in directly working with cloud providers such as AWS, Azure, and GCP is a plus. - Strong problem-solving skills and ability to work collaboratively in a team environment. - Proficiency in operating machine learning solutions at scale, covering the end-to-end ML workflow. - Familiarity with real-world ML systems (configuration, data collection, data verification, feature extraction, resource and process management, analytics, training, serving, validation, experimentation, monitoring). - Obsession with service observability, instrumentation, monitoring, and alerting.