The Implementation Engineer will implement industrial APM and reliability solutions, perform data validation, asset mapping, and support reliability engineering activities at customer sites.
- 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.
- 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.
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