Verified Job
Engineering - Architecture
ML Engineer 2+ YOE
Engineering - Architecture
#49656
Remote / WFH
Axionix Technologies
Job Description
We are looking for a Machine Learning Engineer (3–5 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.
Responsibilities:
• 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)
• Engineer features from multivariate high-frequency time-series data (lag features, rolling stats, domain transforms)
• 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)
• Experience with ML/DL libraries: Scikit-learn, Pandas, NumPy, TensorFlow or PyTorch
• Strong feature engineering skills for time-series data
• Experience deploying models to production
• Excellent analytical and problem-solving skills in industrial contexts
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.
Responsibilities:
• 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)
• Engineer features from multivariate high-frequency time-series data (lag features, rolling stats, domain transforms)
• 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)
• Experience with ML/DL libraries: Scikit-learn, Pandas, NumPy, TensorFlow or PyTorch
• Strong feature engineering skills for time-series data
• Experience deploying models to production
• Excellent analytical and problem-solving skills in industrial contexts
