Director - Artificial Intelligence/Machine Learning (14-19 yrs)

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.