AI/ML Solutions Architect

2 days ago

Chennai, Tamil Nadu, India beBeeDataScience Full time ₹ 1,80,00,000 - ₹ 2,50,00,000
AI/ML Solutions Architect

Job Summary:

This position entails leading the development and implementation of AI and machine learning capabilities to drive business outcomes. The successful candidate will partner with senior leadership to identify strategic opportunities and develop data-driven solutions.

Main Responsibilities:

  • Drive organizational understanding and strategic use of AI/ML across the company.
  • Collaborate with functional heads to analyze business challenges and develop AI-driven solutions.
  • Translate business problems into analytical challenges, building solutions using AI/ML techniques to drive strategic initiatives end-to-end.
  • Develop approaches to scale AI/ML agenda through various AI/ML solutions.
  • Enable data-driven business decisions by presenting insights to top business leaders.
  • Build and manage external relationships for outside-in perspectives and innovative approaches.
  • Drive AI/Analytics projects from conceptualization to implementation and monitoring.
  • Bld and nurture a strong team focused on problem-solving, continuous learning, and experimentation.

Requirements:

  • Master's degree in quantitative fields like mathematics, computer science, engineering, statistics, economics, etc., from top-tier institutes.
  • Minimum 12-15 years of experience in analytical roles, preferably post-graduation.
  • Strong collaboration, analytical thinking, storytelling with data, and project execution skills.
  • Hands-on experience with Analytics/ML/Deep tech/GenAI technologies, open-source tools, and cloud environments.
  • Experience with ML/DL/GenAI frameworks and deep understanding of cloud-based frameworks and services.
  • At least 8 years of experience in client-facing data science consulting roles.

Measures of Success:

  • Ability to form partnerships with functional leaders and influence decision-making through data.
  • Improvement in operational efficiencies and enablement of business goals achievement.
  • Execution of organization-wide initiatives.
  • Number of AI/ML use case implementations.
  • Improvements in algorithm outcomes and model performance.
  • Team engagement, growth, and talent pipeline management.

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