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Vice President - Artificial Intelligence (VP-AI) (New Delhi)

Weekday AI·Posted 1 month ago

Location

Delhi

Experience

7–12 years

Required Skills

Artificial IntelligenceMachine LearningData SciencePredictive ModelingStatistical AnalysisModel DevelopmentEvaluationPythonAI development frameworksData ProcessingFeature EngineeringMLOps conceptsmodel deploymentCloudbased AI platforms

About the Role

This role is for one of the Weekday's clients

Min Experience: 7+ years

Location: Delhi, India, Noida, Uttar Pradesh, India
JobType: full time

We are looking for an experienced and visionary Vice President Artificial Intelligence (VP-AI) to lead the strategy, development, and deployment of cutting-edge AI solutions across the organization. This leadership role is ideal for someone with 712 years of experience in Artificial Intelligence, Machine Learning, and Data Science, who has successfully built and scaled AI-driven products and high-performing engineering teams.

As the VP-AI, you will define the organization's AI roadmap, identify opportunities to leverage AI for business growth, and drive innovation through advanced machine learning techniques. You will collaborate closely with executive leadership, product management, engineering, and business stakeholders to transform complex business problems into scalable AI-powered solutions.

Requirements

Key Responsibilities

  • • Define and execute the organization's long-term AI and Machine Learning strategy aligned with business objectives.

  • • Lead the design, development, deployment, and optimization of AI-powered products and intelligent systems.

  • • Build, mentor, and scale high-performing teams of AI engineers, Machine Learning engineers, Data Scientists, and research professionals.

  • • Drive innovation by identifying emerging AI technologies and evaluating their business applications.

  • • Oversee the complete AI lifecycle, including data collection, feature engineering, model development, deployment, monitoring, and continuous improvement.

  • • Collaborate with product, engineering, and business teams to integrate AI capabilities into customer-facing and internal platforms.

  • • Establish best practices for model governance, experimentation, data quality, and responsible AI development.

  • • Ensure AI systems are scalable, secure, reliable, and optimized for production environments.

  • • Communicate AI strategies, technical decisions, and project outcomes effectively to executive leadership and cross-functional stakeholders.

  • • Foster a culture of continuous learning, experimentation, and innovation across the AI organization.

    • Required Qualifications

  • • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, Statistics, or a related technical field. A PhD is a plus.

  • • 712 years of professional experience in Artificial Intelligence, Machine Learning, and Data Science, with demonstrated leadership experience.

  • • Proven track record of delivering AI-driven products or platforms at scale.

  • • Strong experience managing technical teams and leading cross-functional initiatives from concept to production.

  • • Excellent communication, stakeholder management, and strategic planning skills.

    • Must-Have Skills

  • • Artificial Intelligence

  • • Machine Learning

  • • Data Science

  • • Predictive Modeling

  • • Statistical Analysis

  • • Model Development and Evaluation

  • • Python and AI development frameworks

  • • Data Processing and Feature Engineering

  • • MLOps concepts and model deployment

  • • Cloud-based AI platforms

  • • Team Leadership and Technical Mentoring

  • • AI Strategy and Product Innovation

    • Good-to-Have Skills

  • • Large Language Models (LLMs)

  • • Machine Learning and Deep Learning

  • • Generative AI applications

  • • Natural Language Processing (NLP)

  • • Computer Vision

  • • Reinforcement Learning

  • • Transformer architectures

  • • Retrieval-Augmented Generation (RAG)

  • • AI Agents and Autonomous Systems

  • • Distributed Model Training

  • • GPU Computing and Model Optimization

  • • Experience with enterprise-scale AI deployments .

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