1. End-to-End AI Solution Delivery & Technical Oversight

• Lead the full lifecycle of AI/ML project delivery: from problem formulation and hypothesis generation to data acquisition, feature engineering, model development, deployment, and continuous optimization.
• Design and review advanced statistical models, machine learning algorithms, and deep learning architectures for tasks such as customer segmentation, forecasting, personalization, natural language processing, image/video analysis, and generative content creation.
• Ensure solutions are built for scale, performance, and robustness using modern engineering and MLOps practices—e.g., CI/CD pipelines for models, monitoring dashboards, automated retraining loops.
• Guide model explainability, fairness, and compliance practices in line with Responsible AI guidelines.

2. Strategic Team Leadership & Capability Building

• Manage, mentor, and grow a high-performing team of data scientists and ML engineers by defining clear roles, growth paths, and technical competencies.
• Lead regular code reviews, model design sessions, and innovation sprints to ensure rigor, reusability, and excellence.
• Foster a culture of curiosity, continuous learning, and business alignment, encouraging contributions to internal AI knowledge bases, innovation forums, and external conferences.
• Define and implement team KPIs that measure both technical quality and business impact.

3. Business Problem Translation & Stakeholder Management

• Act as a bridge between business challenges and AI solutions by deeply understanding domain pain points and framing them into solvable data science problems.
• Partner with business stakeholders across functions (e.g., revenue, audience insights, ad sales, content strategy) to identify AI use cases with high ROI.
• Develop compelling narratives and visualizations to communicate results and recommendations to non-technical stakeholders, including senior executives.
• Regularly present outcomes, risks, and learnings to steering committees, ensuring transparency and strategic alignment.

4. AI Productization & Operationalization

• Ensure that models are not just proof-of-concepts but are integrated into production systems with real-time or batch inference pipelines.
• Design scalable APIs, model versioning, and deployment artifacts using best-in-class tooling (e.g., SageMaker, MLflow, Vertex AI, or Kubeflow).
• Work with DevOps and platform teams to standardize model deployment workflows, automate drift detection, and reduce time-to-market for ML products.
• Maintain a post-deployment monitoring framework to track model health, user feedback, and business performance indicators.

5. Governance, Risk Management & Ethical AI

• Partner with Legal, Compliance, and Risk functions to implement guardrails for ethical AI use, especially in sensitive areas like user profiling or content moderation.
• Establish frameworks for model documentation, reproducibility, audit trails, and change control.
• Stay abreast of global AI regulations and proactively align team practices with internal and external compliance requirements

Qualifications & Experiences:

• Master’s degree (or higher) in Computer Science, Data Science, AI/ML, Statistics, or a related field.
• 12–14 years of overall experience in AI/ML or data science, including 7–9 years in managerial or tech-lead roles.
• Demonstrated experience in developing and deploying machine learning models in production environments.
• Proficiency in Python and frameworks like scikit-learn, TensorFlow, PyTorch, or similar.
• Strong knowledge of cloud platforms (preferably AWS or GCP) and containerization tools (Docker, Kubernetes).
• Experience in working with structured and unstructured data, including video, image, and text.
• Solid understanding of data engineering concepts and collaboration with DevOps and engineering teams.
• Excellent communication and stakeholder engagement skills.
• Prior exposure to Media & Entertainment use cases is a plus.