We are looking for a Senior AI/ML Engineer to join our team!
Our client is an innovative technology division within one of the world's top-10 copper producers. The team focuses on leveraging AI, machine learning, and advanced analytics to optimize industrial operations, improve asset reliability, and support data-driven decision-making across global mining and production facilities. The role is focused on building intelligent solutions for predictive maintenance, anomaly detection, and asset performance optimization, helping reduce downtime and improve operational efficiency across large-scale industrial environments.
Responsibilities:
- Design, develop, and deploy machine learning models for predictive maintenance, anomaly detection, and asset health monitoring.
- Build and maintain production-grade ML pipelines for time-series forecasting and sensor/telemetry data processing.
- Collaborate with Data Engineers and Analytics Engineers on data preparation, feature engineering, and model integration.
- Integrate model outputs, risk scores, and predictive insights into operational dashboards and business applications.
- Establish and maintain MLOps processes, including model deployment, monitoring, retraining, and drift detection.
- Research and evaluate new AI/ML approaches, tools, and technologies to improve predictive capabilities and business outcomes.
- Work closely with business and technical stakeholders to translate complex analytical results into actionable recommendations.
Requirements:
- 5+ years of experience as a Machine Learning Engineer or Data Scientist.
- Strong Python skills and hands-on experience with Pandas, NumPy, Scikit-Learn, SciPy, and related ML libraries.
- Proven experience with time-series forecasting and predictive modeling techniques (ARIMA, Prophet, XGBoost, LSTM/RNN, etc.).
- Experience working with telemetry, IoT, sensor, or equipment performance data.
- Hands-on experience with Azure Machine Learning, Databricks, MLflow, or similar MLOps platforms.
- Strong SQL skills and experience working with both relational and non-relational databases.
- Experience building and deploying production-ready ML solutions.
- Strong analytical and problem-solving skills with the ability to translate technical insights into business value.
Nice to have:
- Master's or PhD degree in Data Science, Statistics, Engineering, Mathematics, Operations Research, or a related quantitative field.
- Experience in mining, manufacturing, heavy industry, utilities, or other asset-intensive domains.
- Knowledge of Operational Technology (OT) environments and reliability engineering concepts.
- Experience with enterprise asset management systems such as SAP PM or IBM Maximo.
- Familiarity with reliability methodologies such as RCM or FMEA.
- Experience with real-time data processing and anomaly detection on streaming platforms (Kafka, Azure Stream Analytics, etc.).
We offer*:
- Flexible working format - remote, office-based or flexible
- A competitive salary and good compensation package
- Personalized career growth
- Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
- Active tech communities with regular knowledge sharing
- Education reimbursement
- Memorable anniversary presents
- Corporate events and team buildings
- Other location-specific benefits
*not applicable for freelancers
Skills Required
- 5+ years of experience as a Machine Learning Engineer or Data Scientist
- Strong Python skills
- Hands-on experience with Pandas, NumPy, Scikit-Learn, SciPy
- Proven experience with time-series forecasting and predictive modeling (ARIMA, Prophet, XGBoost, LSTM/RNN)
- Experience working with telemetry, IoT, sensor, or equipment performance data
- Hands-on experience with Azure Machine Learning, Databricks, MLflow, or similar MLOps platforms
- Strong SQL skills and experience with relational and non-relational databases
- Experience building and deploying production-ready ML solutions
- Experience establishing and maintaining MLOps processes (deployment, monitoring, retraining, drift detection)
- Strong analytical and problem-solving skills with ability to translate technical insights to business value
- Master's or PhD in a quantitative field
- Experience in mining, manufacturing, heavy industry, utilities, or other asset-intensive domains
- Knowledge of Operational Technology (OT) environments and reliability engineering concepts
- Experience with enterprise asset management systems such as SAP PM or IBM Maximo
- Familiarity with reliability methodologies such as RCM or FMEA
- Experience with real-time data processing and streaming platforms (Kafka, Azure Stream Analytics)
What We Do
N-iX is a global software solutions and engineering services company that helps world’s leading organizations turn challenges into lasting business value, operational efficiency, and revenue growth using advanced technology. Whether you need to build a custom solution, modernize your digital product or acquire extra tech expertise - we have the experience and capabilities to ensure your success. With over 2,000 professionals in 25 countries across Europe and the Americas, N-iX offers expert solutions in cloud, data analytics, embedded software, IoT, AI, machine learning, and other tech domains. Being in business for over two decades, we have worked with dozens of industry-leading enterprises and Fortune 500 companies creating value across a wide variety of sectors, including finance, manufacturing, supply chain, retail, e-commerce, healthcare, and more. Our unique combination of business domain expertise and technical know-how enables us to effectively collaborate with ISVs, tech companies, and enterprises of all sizes. Thanks to the strong tech ecosystem and partnerships with AWS, GCP, Microsoft, SAP, OpenText, Snowflake, and others, we bring extra speed, scale and efficiency to more than 160 organizations across the globe. N-iX is recognized by numerous industry awards, such as CRN Solution Provider 500, Global Outsourcing 100 by IAOP, ISG Provider Lens™, Modern Application Development services providers by Forrester, etc
