Role Overview

We are seeking a Data & AI Engineer to bridge the gap between the shop floor and advanced analytics.

You will design and deploy production-grade AI solutions that transform high-frequency sensor and machine data into predictive insights.

Your work will directly impact uptime, stability, and Overall Equipment Effectiveness (OEE) across Rosenberger’s global manufacturing footprint.

Key Responsibilities

1.Predictive Maintenance & Asset Reliability

• Advanced Modeling: Develop and deploy models to predict equipment degradation, early failure indicators, and Remaining Useful Life (RUL).

• Maintenance Strategy: Partner with maintenance teams to transition from reactive/scheduled repairs to data-driven, proactive strategies.

• Reliability Engineering: Apply statistical methods to analyze failure patterns and improve machine longevity.

2. Anomaly Detection & Process Optimization

• Deviation Intelligence: Build unsupervised and semi-supervised models to detect machine behavior drift, process instability, and sensor faults.

• Root Cause Analysis (RCA): Use machine data to identify the "why" behind quality drops or throughput losses.

• Real-time Monitoring: Implement AI-driven process monitoring systems to maintain highprecision manufacturing standards. intern | internal

3. Industrial AI Engineering

•Edge & Cloud Deployment: Support the deployment of models in near-real-time environments, balancing edge computing constraints with cloud scalability.

• Pipeline Architecture: Design robust data pipelines that ingest and normalize high-frequency time-series data from PLCs, MQTT brokers, and Industrial Historians.

• Modern ML Stack: Apply deep learning, sequence models, and representation learning to raw sensor signals, ensuring robustness against noisy industrial data.

Technical Requirements :

• Education: Bachelor’s or Master’s in Engineering (Mechanical/Electrical), Computer Science, or Data Science.

• Core Experience: 5+ years in industrial AI, IoT, or machine data processing.

• The Python Expert: Advanced proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow).

• Time-Series Mastery: Deep understanding of signal processing, feature engineering from raw sensors, and handling incomplete/noisy datasets.

• Connectivity: Proven experience integrating data from industrial systems (OPC UA, MQTT, SCADA, or Historians).

Preferred Differentiators :

Edge Intelligence: Experience deploying models on industrial PCs or IoT gateways.

• Domain Knowledge: Understanding of Condition Monitoring, Reliability Engineering, and Manufacturing IT landscapes (MES/ERP).

• Future-Proofing: Familiarity with Digital Twins, simulation-based optimization, or MLOps for the manufacturing floor.

What We Offer:

• Real-World Impact: We solve high-stakes manufacturing problems, not "lab-only" experiments.

• Global Reach: Significant collaboration with our German HQ and international production sites.

• Growth Path: Opportunities to transition into Lead Engineer or Industry 4.0 Architect roles as our Tunisia hub expands.