AWSCloudDockerGoogle Cloud PlatformKubernetesPythonPyTorchScikit-LearnTensorflowAIMachine LearningMLDeep LearningNatural Language ProcessingTensorFlowscikit-learnMLOpsData EngineeringGCPGoogle CloudCI/CDCommunicationCollaboration
About this role
Role Overview
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.
Manage, mentor, and grow a high-performing team of data scientists and ML engineers by defining clear roles, growth paths, and technical competencies.
Act as a bridge between business challenges and AI solutions by deeply understanding domain pain points and framing them into solvable data science problems.
Ensure that models are not just proof-of-concepts but are integrated into production systems with real-time or batch inference pipelines.
Partner with Legal, Compliance, and Risk functions to implement guardrails for ethical AI use.
Requirements
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