Senior Ml/Ai Engineer, Advanced Analytics & Modelling - Customer Intelligence Platforms

What You’ll Do:
We are seeking a highly technical and hands‑on ML/AI Engineer to take end‑to‑end ownership of one of CTC’s strategic customer intelligence platforms. The platform combines machine learning, generative AI and large‑scale transaction data to identify customer shopping missions and behavioural patterns across hundreds of millions of transactions. Its outputs support Personalization, marketing, merchandising, dealer and broader customer analytics use cases.The successful candidate will lead the transition of the third‑party‑developed engine into a fully owned CTC capability. This includes mastering the existing code, prompts, models, data pipelines and architecture;
operating and improvingthe production engine;
and determining where components should be tuned, refactored or rebuilt. The individual will be the technical authority for the platform across CTC and the primary partner to teams that depend on its outputs.Lead the technical transition of the platform and underlying models, ensuring full understanding of the code, documentation, environments, operating knowledge and controls required to own the platform independently.Develop a deep understanding of the full engine, including customer occasion discovery and catalogue generation, transaction tagging, feature engineering, model inference, data flows and downstream outputs.Run the production platform end‑to‑end, including scheduled scoring and catalogue refreshes, model and prompt releases, pipeline monitoring, incident investigation and root‑cause remediation.Own the platform’s ML capabilities, including transaction classification, clustering, feature development, model validation, performance monitoring, and drift management.Own its GenAI capabilities, including prompt architecture, context construction, model selection, prompt and model tuning, structured outputs and catalogue‑generation workflows.Build rigorous ML and GenAI evaluation frameworks using representative test sets, automated measures, human review, regression testing and analysis of failure modes.Implement production controls for GenAI, including guardrails, validation, confidence thresholds, fallback logic, version traceability and monitoring of quality, latency, usage and cost.Write and maintain production‑grade Python and PySpark code across large‑scale data processing, feature engineering, model execution, testing and deployment.Support Personalization and other enterprise use cases, translating requirements into platform enhancements and resolving model, data, integration and production issues.Improve the platform’s accuracy, reliability, scalability, maintainability, runtime, and cost efficiency while ensuring continuity for downstream users.Assess the current architecture objectively and lead the refactoring, redesign or rebuilding of components that do not meet CTC’s long‑term needs.Establish strong software engineering, MLOps and LLMOps practices, including automated testing, deployment, rollback, observability, and technical documentation.What You Bring:
Graduate‑level education in Computer Science, Software Engineering, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Engineering or related technical discipline.5+ years developing, deploying and supporting ML or AI solutions in production, including meaningful hands‑on experience with production GenAI systems.Advanced Python, SQL, PySpark and Spark skills, with experience building maintainable, tested and production‑grade software rather than analytical notebooks alone.Strong foundation in supervised and unsupervised learning, including classification, clustering, feature engineering, model validation, monitoring, and drift detection.Hands‑on production prompt engineering experience, including prompt design, context construction, structured outputs, versioning, regression testing and optimization.Experience selecting, evaluating and tuning foundation LLMs, with a clear understanding of when to use prompt engineering, retrieval, model adaptation or fine‑tuning.Experience evaluating GenAI systems through representative test sets, automated and human review, quality measures, structured error analysis, and production monitoring.Experience implementing GenAI controls, including grounding, guardrails, validation, fallback strategies, observability and model and prompt lineage.Experience with Azure and Databricks, including MLflow, Azure Data Lake Storage, Azure Data Factory, Synapse and related data and deployment services.Experience with CI/CD, automated testing, version control, containerization, and release management for ML and GenAI applications.Strong problem‑solving and root‑cause analysis skills, with the ability to explain model behaviour, limitations and technical trade‑offs to technical and non‑technical partners.Experience with customer intelligence, recommendation systems, and behavioural modelling is an asset.Experience with large‑scale batch inference, re
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