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Media Solution Developer – AI/ML & Automation Focus Role Summary We are seeking a technically strong Media Solution Developer to build AI-powered automation solutions that transform digital media operations. This role focuses on applying AI/ML, NLP, neural networks , and computer vision to automate processes such as campaign setup, QA, reporting, and billing. You will work closely with solution architects to bring intelligent designs to life—improving accuracy, efficiency, and scalability across media workflows. A media background is not required , but deep technical expertise is. Key Responsibilities Design and implement AI/ML solutions that automate repetitive and manual tasks in media operations (e.g., campaign setup, anomaly detection in QA, taxonomy validation, asset analysis). Build and deploy models using machine learning, NLP, and computer vision to improve operational efficiency and decision-making. Develop intelligent automation systems and data pipelines in Python, and integrate them with external advertising platforms via APIs (e.g., Meta, DV360, YouTube). Collaborate with solution architects to convert business problems into scalable, production-ready ML automation solutions. Continuously optimize model and system performance, ensuring reliability and responsiveness in automated workflows. Maintain clean, well-documented code with strong adherence to testing, version control, and compliance standards. Contribute to the broader AI-driven automation strategy across media operations. Ideal Profile: 3–5 years of hands-on experience in machine learning, AI engineering, or data science roles, with a focus on automation. Strong skills in Python, with experience using ML frameworks such as TensorFlow, PyTorch, scikit-learn, and NLP libraries like spaCy or Hugging Face. Experience developing: Automation pipelines using AI/ML to replace or optimize manual media tasks NLP models for text classification, validation, or content tagging Computer vision models for creative asset categorization or quality checks Proven ability to work with APIs and cloud ML platforms (e.g., Google Vertex AI, AWS Sagemaker, Azure ML). Strong understanding of automation architecture and performance optimization in production environments. Ability to work in agile teams and collaborate closely with architects and business stakeholders. Nice to Have: Experience with MLOps (e.g., MLflow, Kubeflow) and deployment orchestration tools (e.g., Airflow, Docker, Kubernetes). Exposure to advertising or marketing tech (DSPs, Meta, Google Ads) is a plus—not mandatory. Familiarity with automation principles in RPA tools (e.g., UiPath) is a bonus, though the primary focus is AI-first automation. Exposure to media buying platforms or AdTech/MarTech ecosystems (DSPs, Meta, Google Marketing Platform). Show more Show less
