Implementation Engineer

Open 30d
  • Implement Industrial APM, predictive maintenance, and reliability solutions for customer sites.
  • Review engineering documents such as P&IDs, PFDs, equipment datasheets, manuals, and historian tag lists.
  • Perform asset-to-tag mapping, data validation, and process-context mapping.
  • Understand and validate process equipment including pumps, compressors, turbines, boilers, heat exchangers, motors, and rotating equipment.
  • Work with DCS, SCADA, PLC, and historian data systems.
  • Support reliability engineering activities including RCM, FMEA, condition monitoring, RCA, and predictive maintenance workflows.
  • Coordinate with customer engineering, maintenance, operations, and digital teams during implementation.
  • Prepare implementation documents, validation reports, mapping sheets, and project updates.
  • Travel to customer sites whenever required.


Requirements

  • 3–5 years of experience in Industrial APM, reliability engineering, process engineering, industrial analytics, or digital transformation projects.
  • Experience in Oil & Gas, Power, Chemicals, Cement, or Process Industries.
  • Good understanding of industrial processes and equipment behaviour.
  • Ability to read and understand P&IDs, PFDs, datasheets, manuals, and tag lists.
  • Exposure to DCS/SCADA/PLC/Historian systems.
  • Knowledge of condition monitoring, predictive maintenance, RCM, FMEA, and RCA concepts.
  • Strong Excel and data validation skills.
  • Good communication and customer coordination skills.

Preferred Skills

  • Experience with APM or Predictive Maintenance platforms.
  • Exposure to OSIsoft PI / AVEVA PI, Aspen IP.21, Honeywell PHD, GE Historian, or similar systems.
  • Familiarity with Honeywell, Emerson, Siemens, ABB, Yokogawa, Schneider, or Rockwell DCS systems.
  • Exposure to SAP PM, IBM Maximo, or other CMMS platforms.
  • Experience in power plant systems or rotating equipment monitoring is an added advantage.
  • Basic understanding of industrial analytics, dashboards, or ML-based monitoring systems.