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Process Engineer
Navi Mumbai, India
Project Manager / Service Delivery Lead
The Process Engineer is responsible for reviewing, understanding and following customer inquiries and technical specifications. This person has overall responsibility with the design of oil and gas field equipment and technologies and performs project execution. The role includes field operations and treatments, as well as implementing, monitoring, and controlling different solutions to meet project and client expectations. The objective of the position is to support sales, service, delivery, and technical support of clients in assigned geographical areas and territory.
Qualification: Bachelor’s Degree in Chemical, Petroleum, or Petrochemical Engineering or equivalent; higher degree preferred.
Experience: 7–12 years in Process Engineering within E&P / Oil & Gas / Service industry, with a mix of Operator, Licensing, and Consultancy exposure.
Project Types: Hands‑on experience in Concept Development, FEED, Pre‑FEED, Detail Engineering and Digital projects for onshore and offshore upstream Oil & Gas projects.
Language: Proficiency in English and Hindi required.
Process Simulation
Flow Assurance
Digital Twin / AI
Productivity
Symmetry or industry equivalent
OLGA
PIPESIM
Digital Twin / Asset Performance
Microsoft Excel (Advanced)
Hands‑on experience with a specific digital twin or asset performance management platform is advantageous but not mandatory. Candidates with strong process engineering foundations and demonstrated exposure to simulation tools, real‑time monitoring environments, or AI‑assisted engineering workflows will be considered.
Understanding of digital twin concepts for upstream surface facilities – virtual models of processing equipment, pipelines, and entire facility networks that mirror real‑world performance in real time.
Ability to use equipment and facility twin outputs to track performance against design baselines, support engineering decisions, adequacy studies, and design reviews.
Working knowledge of physics‑based modelling, data‑driven AI techniques, and hybrid (physics‑informed AI) approaches applied in facility and equipment twins.
Familiarity with cloud‑based digital twin deployment and integration with operational data from multiple OEM or vendor systems.
Ability to apply digital twin insights to identify performance gaps, support commissioning, and enhance engineering documentation.
Ability to use digital process twins and simulation tools for real‑time and predictive optimization of facility operations.
Experience in ‘what‑if’ scenario analysis using simulation models and digital twins to maximize production, optimize energy usage, and reduce emissions.
Understanding of AI‑assisted optimization workflows – automated insights that reduce OPEX, increase throughput, and improve energy efficiency.
Ability to interpret AI‑generated advisory outputs and translate them into practical process engineering actions for production assurance and performance improvement.
Knowledge of integrating digital optimization tools with conventional process engineering workflows – PFDs, P&IDs, steady‑state and dynamic simulation models.
Awareness of emissions performance monitoring and carbon footprint optimization as part of process system design and operations.
Familiarity with condition‑based monitoring, continuous assessment of equipment health using real‑time sensor data and AI‑driven anomaly detection.
Understanding of prognostic health management (PHM), applying predictive failure models and machine learning to anticipate equipment issues ahead of occurrence.
Ability to interpret failure prediction and anomaly detection outputs and translate them into actionable maintenance recommendations and engineering responses.
Knowledge of facility‑wide health management analytics, integrating data across compressors, pumps, separators, pipelines, and process equipment into a unified monitoring view.
Familiarity with maintenance planning workflows – using real‑time equipment data and AI insights to plan, prioritize and optimize maintenance activities.
Ability to collaborate with reliability engineers, OEM specialists, and operations teams to convert digital health monitoring insights into engineering actions.