Job Summary

Startup Inno is seeking a highly motivated AI Machine Learning Engineer to design and deploy intelligent predictive maintenance solutions that enhance equipment reliability and operational efficiency. In this role, you will work at the intersection of machine learning, data engineering, and industrial analytics to build scalable models that anticipate failures before they occur. The ideal candidate combines strong technical expertise with practical experience in time-series analysis, anomaly detection, and production-grade ML systems.

Key Responsibilities

• Design, develop, and deploy machine learning models for predictive maintenance and failure forecasting.

• Analyze large-scale sensor and IoT datasets to identify patterns, anomalies, and degradation signals.

• Build and optimize end-to-end ML pipelines, including data ingestion, feature engineering, model training, and monitoring.

• Collaborate with data engineers, product managers, and domain experts to translate business needs into AI solutions.

• Implement time-series forecasting and anomaly detection techniques for industrial equipment.

• Improve model accuracy, robustness, and scalability in real-world production environments.

• Develop model evaluation frameworks and continuously monitor model performance.

• Document technical designs, experiments, and deployment processes.

• Stay current with advancements in machine learning, edge AI, and predictive analytics.

Required Skills and Qualifications

• Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field.

• Strong programming skills in Python and familiarity with ML libraries such as TensorFlow, PyTorch, or scikit-learn.

• Experience with time-series analysis, anomaly detection, or predictive maintenance use cases.

• Solid understanding of statistics, probability, and machine learning fundamentals.

• Experience working with large datasets and data processing tools (e.g., Pandas, Spark).

• Familiarity with cloud platforms (AWS, Azure, or GCP) and MLOps practices.

• Knowledge of SQL and data pipeline development.

• Strong problem-solving and analytical thinking skills.

Experience

• 3–6 years of hands-on experience in machine learning or data science roles.

• Proven experience deploying ML models into production environments.

• Prior work with industrial IoT, manufacturing, energy, or asset-heavy industries is highly preferred.

• Experience with real-time or streaming data systems is an advantage.

Working Hours

• Full-time position.

• Flexible remote or hybrid work options available.

• Must be available for core collaboration hours with global teams.

Knowledge, Skills, and Abilities

• Deep understanding of predictive modeling and equipment health analytics.

• Ability to translate complex data into actionable insights.

• Strong communication skills with the ability to explain technical concepts to non-technical stakeholders.

• Self-driven with the ability to manage multiple priorities in a fast-paced startup environment.

• Familiarity with MLOps, CI/CD for ML, and model monitoring tools.

• High attention to detail and commitment to quality.

Benefits

• Competitive salary and performance-based incentives.

• Flexible work arrangements (remote-friendly).

• Health and wellness benefits.

• Learning and development budget for certifications and courses.

• Opportunity to work with cutting-edge AI technologies.

• Collaborative and innovation-driven work culture.

Why Join

At Startup Inno, you will be part of a forward-thinking team building impactful AI solutions for real-world industrial challenges. This role offers the opportunity to work on meaningful predictive maintenance systems that reduce downtime, save costs, and transform how organizations manage critical assets. If you are passionate about applied AI and want to see your models deliver measurable business value, this is the place to grow your career.

How to Apply

Interested candidates should submit their updated resume, portfolio (if available), and a brief cover letter outlining relevant experience in predictive maintenance or time-series machine learning. Applications will be reviewed on a rolling basis. Qualified candidates will be contacted for technical evaluation and interviews.