Senior Machine Learning Engineer, Applied Intelligence

Job description

Job responsibilities

• Architect and own the AI/ML platform stack—from data ingestion, labeling, and feature engineering to model training, deployment, monitoring, and lifecycle management for factory sensing and intelligent automation…
• Select, prioritize, and standardize industrial AI components including feature stores, vector databases for RAG pipelines, OCR/IDP and computer vision model serving, orchestration layers, and observability systems.
• Build model-serving and inference frameworks optimized for production environments, supporting real-time and batch execution across cloud, edge, and shop-floor systems.
• Partner with manufacturing engineers and factory operators to understand production workflows and translate them into MLOps requirements.
• Write production-quality code with comprehensive tests, participating in code review and architectural discussions.
• Translate factory scenarios (quality inspection, receiving, root-cause analysis, document processing) into applied AI workflows with defined human-in-the-loop gates, audit trails, and integration contracts with PLM,…
• Implement event-driven data pipelines and telemetry systems that feed models with contextualized, real-time signals from factory sensors, production systems, and logistics operations.
• Deploy and operate your systems in factory environments, including edge compute clusters and OT networks.

Job requirements

• 8+ years of experience in a software engineering role building production systems, ideally in a fast-paced environment.
• Deep expertise in MLOps with end-to-end experience delivering production-grade AI/ML systems.
• Strong technical fluency in modern software architectures, APIs, distributed systems, CI/CD, and cloud or edge infrastructure.
• Deep experience with MLOps: data acquisition, labeling, curation, pipeline management, model versioning, continuous integration, and model monitoring.
• Strong proficiency in Python and experience with deep learning frameworks (PyTorch, TensorFlow).
• Experience building and deploying containerized ML services using Docker and Kubernetes.
• Proficiency in data engineering, time-series data modeling, and working with semantic/ontology-driven data systems.
• Experience implementing observability for model performance, inference accuracy, and data drift.