AVEVA PI

Job Title: Technical Lead

Location: Houston, TX (Remote or Hybrid)

Hourly Rate :- $80/hr.

AVEVA PI and Process Digital Twin Engineer to design, develop, validate, and support industrial digital twin solutions. The role will integrate real-time and historical plant data from AVEVA PI System with first-principles process models, preferably developed in Aspen HYSYS, to enable process monitoring, performance analysis, scenario evaluation, and operational optimization. The successful candidate will combine strong process engineering knowledge with industrial data-platform expertise and disciplined model and data quality practices.

Lead the design, development, configuration, and deployment of process digital twin solutions using AVEVA PI System and process simulation platforms. Integrate real-time and historical plant data with process models to support model calibration, performance monitoring, what-if analysis, forecasting, and optimization use cases. Develop, configure, and maintain AVEVA PI components, including PI Data Archive, Asset Framework, Asset Analytics, Event Frames, Notifications, PI Interfaces/Connectors, PI Web API, and related client tools. Create and enhance PI AF hierarchies, templates, attributes, analyses, calculations, event logic, and reusable asset models. Develop or maintain steady-state and/or dynamic process simulation models, preferably using Aspen HYSYS, based on process flow diagrams, piping and instrumentation diagrams, equipment data sheets, operating philosophies, and plant operating data.
Define tag-to-model mappings and establish reliable data exchange between PI, simulation engines, control systems, databases, and downstream analytics or visualization applications.
Perform process model calibration, reconciliation, sensitivity analysis, validation, and performance benchmarking against actual plant data. Design PI Vision displays, trends, dashboards, and contextual views for operations, engineering, reliability, and management users. Establish and execute QA/QC procedures for source data, tag mappings, calculations, simulation outputs, dashboards, and digital twin releases. Identify data-quality issues such as missing values, stale tags, bad quality flags, unit mismatches, timestamp misalignment, outliers, and sensor drift; define appropriate remediation or exception-handling rules.