Principal consultant engineer

01 ago - Italia
Experteer Italy

Experteer Overview In this role you will lead the development of simulation models for industrial processes and utilities to support optimization, monitoring, and energy/emissions reduction. You will work within the Global Professional Services ENG Group, collaborating with cross‐functional teams to deliver impactful projects for clients in the process industries. You'll steer medium to large deployments, mentor others, and contribute to R&D; and product management as a subject‐matter expert. A key hook is shaping practical, data‐driven solutions that scale across client operations while advancing Aspen Tech's sustainability and digital twin initiatives. Responsibilities Serve as technical lead on medium to large projects and resolve complex issues to ensure customer satisfaction Coordinate solution delivery with Aspen Tech Project Manager and client to meet milestones, delivery dates, and financial goals Oversee multiple projects and mentor less experienced team members Review process flow diagrams, PFDs, mass balances,



and plant data to inform modeling Implement projects using Aspen Tech tools (Aspen Plus, Aspen Plus EO, HYSYS, Aspen On Line, etc.) Lead RTO and Digital Twin deployment for open/closed‐loop optimization and sustainability Participate in client meetings, prepare project documentation, and assist with user training Communicate results to clients and collaborate with Sales, Support, Product Management and R&D; Maintain client satisfaction and identify opportunities for additional services Assist in preparing and delivering complex bids and proposals to major corporate clients Qualifications Bachelor in Chemical Engineering with 8+ years in process modeling for Refining, Petrochemical or Ethylene Experience with Aspen Tech Engineering Suite and/or comparable tools Proven ability to model complex plants (e.g., ethylene, ammonia, refinery) Knowledge of APC and Historian (IP.21) is advantageous Experience with RTO/Digital Twin deployments and data‐driven opt

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