AI Engineer � Manufacturing & Quality Systems

Job Description

• Design, develop, and operationalize AI/ML solutions that optimize manufacturing performance, product quality, and predictive maintenance using state-of-the-art frameworks (Vertex AI, TensorFlow, PyTorch, Databricks ML).
• Build and automate ML pipelines integrating structured, unstructured, and streaming data from industrial systems (MES, SCADA, PLC, IoT).
• Develop, train, and deploy models for computer vision, time-series forecasting, and anomaly detection in production environments.
• Implement MLOps practices: model versioning, monitoring, retraining, and drift detection-ensuring scalability, transparency, and regulatory compliance.
• Collaborate with data engineers and business stakeholders to translate operational challenges into measurable AI use cases.
• Contribute to digital twin initiatives and real-time process optimization by embedding AI into automated manufacturing workflows.
• Optimize compute environments and model inference architectures for performance, latency, and cost efficiency.
• Champion responsible and explainable AI principles, ensuring ethical and auditable deployment of machine learning models.
• Document model architecture, experimentation outcomes, and operational metrics in alignment with enterprise AI governance frameworks.

Job Requirements

• 4+ years of experience designing, developing, and deploying AI/ML solutions using frameworks such as Vertex AI, TensorFlow, PyTorch, or Databricks ML.
• 4+ years of experience building automated ML pipelines integrating structured, unstructured, and streaming data.
• 3+ years of experience working with industrial/operational data systems (MES, SCADA, PLCs, IoT).
• 4+ years of hands-on experience developing models in:
  • Computer vision
  • Time-series forecasting
  • Anomaly detection
• 3+ years of experience implementing MLOps practices (model versioning, monitoring, CI/CD, retraining, drift detection).
• 3+ years of experience deploying ML models into production environments and optimizing inference performance for cost and latency.
• 2+ years of experience supporting or contributing to digital twin systems or real-time process optimization initiatives.

• 5+ years of experience designing scalable ML system architectures across cloud or hybrid environments.
• 3+ years documenting models, experiments, and metrics in compliance with enterprise governance frameworks.

• Experience with cloud architecture (GCP preferred), Kubernetes, and containerization.
• Understanding of audit, compliance, and regulatory requirements for operational AI systems.