Implementation Engineer

• 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.