ML Architect – AI & Automation Europe

The global jewelry manufacturer and retailer is seeking a highly experienced Machine Learning Architect to define and lead the architectural vision for the applied ML ecosystem. This role focuses on driving forecasting, predictive modeling, and production-grade MLOps capabilities across the organization. You will shape the next-generation ML platforms, ensuring that forecasting, optimization, and decision‑intelligence use cases are robust, scalable, and seamlessly integrated into the global retail environment.

As a senior ML leader, you will collaborate across engineering, data, supply chain, merchandising, and digital functions to establish best‑in‑class ML patterns, champion model lifecycle excellence, and advance Pandora’s enterprise ML maturity.

Guide ML direction across forecasting, optimisation, recommendations, and predictive analytics.

Translate industry trends in time‑series modelling, probabilistic forecasting, causal ML, feature stores, and MLOps automation into actionable architectural strategies.

Align ML ecosystem development with business priorities across merchandising, supply chain, operations, and omnichannel retail.

Lead the design and architecture of ML systems, including forecasting pipelines, demand/sales/promotions prediction models, anomaly detection systems, and real‑time scoring services.

Partner with engineering and AI & Data platforms teams to ensure models integrate into operational systems with robust SLAs and monitoring.

Define reference architectures for traditional ML, deep learning, and time‑series forecasting.

Architect end‑to‑end MLOps pipelines covering training, evaluation, deployment, and monitoring for high‑scale model fleets.

Ensure reproducibility, CI/CD for ML, automated retraining, feature store integration, and governance of model lineage.

Drive platform choices and integrations (e.g., Databricks, Fabric, MLflow, Kubernetes‑based serving).

Evaluate third‑party forecasting and ML SaaS solutions and guide integration into internal systems.

Lead architectural assessments and drive decisions on build vs buy for ML capabilities.

Define and enforce architectural and MLOps standards that ensure models meet NFRs such as accuracy, performance, fairness, reliability, and explainability.

Champion ML governance, including model risk management, bias detection, privacy compliance, and ethical AI.

Work closely with the Data & AI Platform, retail analytics, supply chain and merchandising teams to ensure ML solutions are scalable and production‑ready.

Align model design with data pipelines, real‑time event streaming, microservices, and operational workflows.

Provide hands‑on support (~20% time) for prototype forecasting models, PoCs, architecture spikes, and code reviews.

Mentor engineers and data scientists on best practices in ML architecture and MLOps.

Extensive experience designing and deploying ML architectures, including forecasting, predictive modelling, and MLOps.

Strong background in traditional ML, time‑series modelling, and ML productionisation at enterprise scale.

Prior experience as an ML Architect, Solutions Architect, Machine Learning Engineer, or similar senior role.

Expertise with forecasting frameworks (e.g., Prophet, ARIMA, DeepAR, TFT, custom deep learning forecasting architectures).

Strong understanding of ML lifecycle management, feature stores, ML observability, and performance optimisation.

• Databricks, Fabric, Spark
• MLOps frameworks (MLflow, CI/CD pipelines, containerisation, serving patterns)
• Infrastructure as Code (Terraform, ARM/Bicep)
• Event‑driven and microservices architectures for real‑time inference

Excellent communicator able to bridge ML complexity with business and engineering clarity.

Strong leadership presence, enabling cross‑functional alignment and influencing strategic decisions.

Azure / AWS / GCP ML or AI certifications.

Experience with ML governance, risk management, explainability, and cost‑optimised ML architectures.