ML Architect (AI & Automation)

TL;DR

ML Architect (AI & Automation): Defining and leading the architectural vision for an applied ML ecosystem with an accent on forecasting, predictive modeling, and production-grade MLOps capabilities. Focus on shaping next-generation ML platforms, ensuring robustness, scalability, and seamless integration into a global retail environment.

Location: Central Europe

Company

A global jewelry manufacturer and retailer is seeking to define and lead the architectural vision for its applied ML ecosystem.

What you will do

• Guide ML direction across forecasting, optimization, recommendations, and predictive analytics.
• Lead the design and architecture of ML systems, including forecasting pipelines and real-time scoring services.
• Architect end-to-end MLOps pipelines covering training, evaluation, deployment, and monitoring.
• Evaluate and integrate third-party ML SaaS solutions and drive build-vs-buy decisions.
• Define and enforce architectural and MLOps standards for accuracy, performance, reliability, and ethical AI.
• Provide hands-on support for prototype models, PoCs, architectural spikes, and code reviews (approx. 20% time).

Requirements

• Extensive experience designing and deploying ML architectures, including forecasting, predictive modeling, and MLOps.
• Strong background in traditional ML, time-series modeling, and ML productionization at enterprise scale.
• Prior experience as an ML Architect, Solutions Architect, or Senior Machine Learning Engineer.
• Expertise with forecasting frameworks (e.g., Prophet, ARIMA, DeepAR, TFT).
• Hands-on experience with Cloud ML platforms (Azure preferred), Databricks, Fabric, and Spark.
• Experience with MLOps frameworks (MLflow, CI/CD pipelines, containerization, serving patterns), and Infrastructure as Code (Terraform, ARM/Bicep).
• Excellent communication and strong leadership presence to bridge ML complexity with business and engineering clarity.

Nice to have

• Azure / AWS / GCP ML or AI certifications.
• Experience with ML governance, risk management, explainability, and cost-optimized ML architectures.

Culture & Benefits

• Paid Vacation
• Sick Days
• Floating Holidays
• Sport/Insurance Compensation
• English Classes
• Charity
• Training Compensation