AI-powered predictive analytics for industrial operations and maintenance
Shape delivers predictive maintenance and reliability solutions to oil and gas operators using Python, TensorFlow, PyTorch, and Spark. The tech stack reflects a data-science-first architecture: MLflow and Kuberflow for model deployment, OSIsoft PI and SCADA integrations for industrial sensor data, and SAP/Maximo for maintenance workflows. Active hiring skews heavily toward senior engineers and data roles (7 engineering, 2 data), with near-term focus on building an internal MLOps platform and scaling an AI platform architecture—signaling maturation beyond point solutions toward a standardized, deployable model stack.
Shape is a digital services company spun from MODEC, operating in Rio de Janeiro with a team of 51–200. The company targets capital-intensive industries—primarily oil and gas—with AI-driven solutions for predictive maintenance, asset monitoring, energy efficiency, and process safety. Work combines first-principles modeling with machine learning on industrial time-series data (PI system, SCADA) integrated into existing ERP and maintenance systems (SAP, Maximo, Oracle). The org blends systems engineers, data scientists, and full-stack developers to customize solutions for client-specific operational constraints.
Core stack includes Python, Scala, Java, C++, TensorFlow, PyTorch, scikit-learn, MLflow, Kubeflow, and Apache Spark for ML/data. Industrial integrations: OSIsoft PI, SCADA, SAP PM, Maximo. Cloud: Azure, AWS, GCP. ERP: SAP, Oracle, Workday.
Building an MLOps platform, implementing AI platform architectures, integrating AI across product domains, deploying predictive maintenance monitoring, and automating financial/HR/legal processes.
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