Overview

ML Engineer (Time Series Mandatory) | 2+ YOE

Responsibilities

• We are looking for a Machine Learning Engineer (35 years experience) with strong expertise in time-series modeling, MLOps, Python, and SQL to build and deploy industrial AI solutions for refinery and petrochemical operations.
• You will work closely with Data Scientists, Data Engineers, and Process SMEs to convert high-frequency process data into real-time AI systems that deliver measurable business impact such as energy savings, yield improvement, throughput optimization, and emissions reduction.
• This role is ideal for someone who enjoys working at the intersection of process engineering + machine learning + production deployment.
• Design, build, and deploy end-to-end AI/ML pipelines for industrial use cases at scale (cloud or on-prem)
• Develop time-series forecasting and prediction models for process variables (temperature, pressure, yield, energy, emissions)
• Build optimization models for refinery and petrochemical operations (fuel optimization, energy efficiency, throughput maximization)
• Build reliable data pipelines using Python & SQL connecting IT/OT systems (PHD, OPC, SCADA, historians)
• Deploy models via APIs, batch pipelines, or real-time streaming
• Implement model monitoring, drift detection, CI/CD, automated retraining
• Collaborate with process engineers and operations teams to translate domain problems into ML solutions

Qualifications

• 2+ years of experience in ML/AI engineering roles
• Strong Python programming and SQL expertise
• Proven experience in time-series forecasting and prediction
• Hands-on experience building end-to-end ML systems (data model deployment monitoring retraining)
• Solid understanding of MLOps (model versioning, CI/CD, monitoring, retraining)
• Strong feature engineering skills for time-series data
• Excellent analytical and problem-solving skills in industrial contexts .