Machine Learning / AI Engineer

Department: Engineering – AI & Machine Learning

We are looking for a hands-on Machine Learning / AI Engineer who can convert business problems into production-ready ML and AI solutions. You will work closely with Product, Engineering, Data, and Ad Operations teams to develop models, data pipelines, APIs, experiments, and intelligent automation systems for Lemma's advertising platform. This role is suitable for someone who enjoys working across data exploration, model development, software engineering, experimentation, and production deployment.

• Analyse large advertising datasets to identify patterns, opportunities, and anomalies.
• Build and evaluate machine learning models for use cases such as:
• Bid-response and win-rate prediction
• Inventory and campaign forecasting
• Floor-price and margin optimisation
• Audience classification and targeting
• Traffic-quality and anomaly detection
• Campaign pacing and performance optimisation
• Creative and inventory intelligence
• Develop data preprocessing, feature-engineering, training, and inference pipelines.
• Build APIs and services to integrate ML models with production applications.
• Design offline evaluations and online experiments to measure model impact.
• Monitor model accuracy, drift, latency, reliability, and business KPIs.
• Work with structured, semi-structured, geospatial, time-series, and event-stream data.
• Contribute to Generative AI use cases involving:
• Retrieval-Augmented Generation
• AI agents and workflow automation
• Campaign-planning assistants
• Natural-language interfaces for analytics and reporting
• Collaborate with backend engineers to deploy models in scalable, low-latency environments.
• Document experiments, assumptions, model behaviour, APIs, and deployment processes.
• Participate in code reviews, design discussions, testing, and production troubleshooting.

Qualifications: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Statistics, or a related technical discipline. Equivalent practical experience will also be considered.

• 2–5 years of professional experience in Machine Learning, Data Science, Applied AI, or a related engineering role.
• Strong programming skills in Python.
• Good understanding of:
• Supervised and unsupervised learning
• Classification, regression, clustering, and ranking
• Feature engineering and model evaluation
• Statistics and probability
• Time-series analysis and forecasting
• Hands-on experience with libraries such as:
• Pandas
• NumPy
• Scikit-learn
• XGBoost, LightGBM, or CatBoost
• Working knowledge of deep-learning frameworks such as PyTorch or TensorFlow.
• Strong SQL skills and experience working with large datasets.
• Experience building REST APIs or backend services using Python frameworks such as FastAPI or Flask.
• Familiarity with Git, Docker, Linux, unit testing, and CI/CD practices.
• Ability to explain model outcomes and technical findings to non-ML stakeholders.
• Strong analytical, debugging, and problem-solving skills.