About the role

We're seeking an Industrial AI Engineer to build and deploy production-grade AI/ML solutions that improve safety, reliability, product quality, and energy efficiency across petrochemical manufacturing operations. You will work closely with Process Engineers, Production, Process Technology, and Digital/IT teams to develop models and deliver them into real plant workflowsintegrated with historians, control systems, and decision-making processes.

This role combines AI and machine learning expertise with practical understanding of chemical processes and industrial data, including time-series signals, lab data, operating modes, and constraints in continuous manufacturing.

Key responsibilities

Develop and deploy AI/ML solutions for manufacturing use cases such as:
Process optimization (yield, energy, throughput, quality targets, constraint handling)
Process anomaly detection and early warning systems for abnormal situations
Soft sensors / virtual analyzers to estimate unmeasured quality variables
Vision AI for safety, inspection, leakage/flare/smoke detection, and operational monitoring
Agentic AI solutions (e.g., copilots for operators/engineers, workflow automation with guardrails)
Translate plant problems into problem statements, define KPIs, and build measurable use cases
Build end-to-end pipelines: data extraction, cleaning, feature engineering, model training, validation, deployment, monitoring, and retraining
Work with plant data sources (e.g., PI historian, LIMS, MES, APC/PLC/DCS interfaces) and ensure data quality and contextualization (tags, units, equipment hierarchy)
Implement MLOps practices (versioning, testing, CI/CD, model monitoring, drift detection, documentation)

Required qualifications

Bachelor's or Master's degree in Chemical Engineering, Computer Science, Data Science, or related field
Experience building ML models or industrial analytics solutions in manufacturing/energy/chemicals (preferred)
Strong programming skills in Python (data wrangling, modeling, APIs) and Database (SQL), familiar with cloud platform Azure, AWS. Google Cloud
Solid ML foundation: regression/classification, clustering, time-series methods, anomaly detection, model evaluation, and feature engineering
Familiarity with process data and chemical manufacturing context (unit operations, process constraints, plant variability, operating modes, safety considerations)
Experience with: REST APIs, git version control, CI/CD, Docker
Self-driven and eager to continuously learn and stay current with emerging AI/ML technologies, able to upskill independently and proactively propose practical solutions

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