Director - Artificial Intelligence/Machine Learning

Responsibilities : - Develop and implement data science strategies to drive business growth. - Collaborate with cross-functional teams to identify opportunities for data-driven insights and solutions. - Lead the development of machine learning models using Python and other relevant tools. - Conduct regular model maintenance and optimization to ensure accuracy and efficiency. - Stay up-to-date with emerging trends and best practices in data science. - Build cutting-edge capabilities utilizing machine learning and data science (e. g., large language models, computer vision models, voice models, etc. ). - Mentor, guide, coach, coordinate training opportunities, and support individual growth and technical skills across the engineering teams. - Enable a continuous learning environment to keep abreast of industry trends. - Partner with research organizations and explore innovation opportunities. - Leverage industry best practices and tools to continually improve teams' ability to build, operate and maintain products. - Ensure that technical solutions are in line with established company strategy, and standards for architecture, security, corporate governance, coding standards, monitoring, logging, unit test, and service enablement. - You will be responsible for overseeing the ML strategy, vision, and roadmap of your organization. - You will also be leading and mentoring a team of ML professionals, and collaborating with other teams and departments. - You are expected to grow a strong bench of ML/AI leaders to help Sprinklr further expand its ML/AI footprint in the company. Requirements : - Bachelor's or master's degree in computer science, mathematics, statistics, or related field. - 12 years of experience designing, building, deploying, testing, maintaining, monitoring, and owning scalable, resilient, and distributed machine learning algorithms and 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, and monitoring). - Obsession for service observability, instrumentation, monitoring, and alerting. (ref:hirist.tech)

Location: in, IN

Posted Date: 11/23/2024