ML Engineer (Time Series Mandatory) | 2+ YOE

Company Description
Axionix Technologies is a global digital transformation solutions provider that collaborates with leading technology companies to drive meaningful change. The organization partners with clients from solution design through ongoing operations, combining advanced technology with a people-focused and purpose-led approach. With strong domain expertise and an emphasis on future-ready strategies, Axionix integrates innovation and agility into client ecosystems. Axionix’s product portfolio includes EduKit (LMS & ERP for schools), PropCRM (real estate CRM), Towing Master (urban towing SaaS), and Marketika (omnichannel communication platform), offering diverse opportunities to work on real-world applications.
Role 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

Work arrangement
No