Senior / Lead Machine Learning & AI Engineer

Department: Engineering – AI & Machine Learning

We are looking for a Senior / Lead Machine Learning & AI Engineer to define, build, and productionise ML and AI capabilities across Lemma's advertising platform. This is a hands-on technical leadership role. You will work on ambiguous, high-impact problems involving large-scale advertising data, real-time decision systems, optimisation, forecasting, and Generative AI. You will be responsible not only for developing models, but also for designing reliable ML systems, defining evaluation strategies, influencing the product roadmap, mentoring engineers, and ensuring that ML investments produce measurable business outcomes.

ML and AI Solution Ownership

• Own ML and AI initiatives from business-problem discovery through experimentation, production deployment, and continuous improvement.
• Translate advertising and platform challenges into clearly defined ML problems, metrics, and technical roadmaps.
• Architect solutions for use cases such as:
• Real-time bid-response prediction
• Dynamic floor-price and margin optimisation
• Campaign pacing and delivery optimisation
• Inventory, demand, impression, and revenue forecasting
• Audience modelling and lookalike generation
• Supply-path and traffic-quality optimisation
• Invalid-traffic and anomaly detection
• Creative intelligence and contextual classification
• Recommendation and ranking systems
• Automated campaign planning and optimisation

Production ML Engineering

• Design scalable training, feature-engineering, model-serving, and monitoring architectures.
• Develop batch, streaming, and low-latency inference systems.
• Establish standards for model versioning, reproducibility, deployment, rollback, observability, and governance.
• Ensure ML services meet production expectations for latency, throughput, availability, accuracy, and cost.
• Work with platform engineers to integrate models into real-time ad-serving and decision-making workflows.
• Design systems that combine traditional ML, optimisation algorithms, and business rules where appropriate.
• Define offline and online evaluation frameworks for ML initiatives.
• Design A/B tests, holdout experiments, and incremental-impact measurement.
• Connect model metrics with business outcomes such as:
• Win rate
• Response rate
• Fill rate
• Revenue
• Publisher yield
• Advertiser ROI
• Campaign delivery
• Margin
• Forecast accuracy
• Operational efficiency
• Investigate model degradation, bias, drift, unexpected behaviour, and production discrepancies.

Generative AI and Intelligent Automation

• Architect production-grade Generative AI systems using LLMs, embeddings, retrieval, structured data, and tool-based agents.
• Build AI capabilities for campaign planning, analytics, reporting, inventory discovery, engineering productivity, and operational automation.
• Evaluate models and frameworks based on accuracy, latency, cost, privacy, reliability, and maintainability.
• Establish guardrails, evaluation datasets, human-approval workflows, and monitoring for AI-generated outputs.
• Identify where deterministic systems or traditional ML are more appropriate than Generative AI.

Qualifications: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Statistics, or a related technical discipline. A Master's degree is preferred but not mandatory for candidates with strong production experience.

• 6–9 years of professional experience in Machine Learning, Applied AI, Data Science, or ML Engineering.
• Demonstrated experience deploying and operating ML models in production.
• Expert-level Python programming skills.
• Strong software-engineering fundamentals, including:
• Data structures and algorithms
• Object-oriented and modular design
• API development
• Testing and code quality
• Distributed-system fundamentals
• Performance and reliability engineering
• Strong knowledge of:
• Classification, regression, ranking, and recommendation systems
• Time-series forecasting
• Optimization techniques
• Statistical inference and experimentation
• Feature engineering and model evaluation
• Model explainability, drift, and monitoring
• Hands-on experience with frameworks and tools such as:
• Scikit-learn
• XGBoost, LightGBM, or CatBoost
• PyTorch or TensorFlow
• Pandas and NumPy
• Advanced SQL skills and experience working with high-volume datasets.
• Experience designing batch and real-time data or ML pipelines.
• Experience building model-serving APIs and scalable inference systems.
• Strong understanding of cloud infrastructure, containers, CI/CD, and production observability.
• Ability to balance model sophistication with latency, cost, explainability, and business impact.
• Strong written and verbal communication skills.