Machine Learning Engineer – Technical Lead

We are seeking an experienced Machine Learning Engineer – Technical Lead to join our partner's team. In this role, you will lead the design and delivery of advanced machine learning solutions for industrial IoT applications, while mentoring a team of engineers and data scientists to build scalable, production-ready systems.

Key Responsibilities:

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  Design, develop, and deploy machine learning models for:
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    predictive maintenance,
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    anomaly detection,
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    asset optimization,
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    and time-series forecasting.
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  Work with large-scale sensor and telemetry data collected from connected devices.
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  Build reliable data pipelines and real-time inference systems integrated across cloud and edge environments.
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  Lead the full lifecycle of ML initiatives, from solution design and experimentation to deployment and optimization.
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  Provide technical leadership and mentorship to ML and software engineering teams, promoting best practices in model development, testing, and deployment.
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  Collaborate closely with product managers, architects, and domain experts to ensure technical solutions align with business objectives.

Required Qualifications:

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  Bachelor's degree in Computer Science, Electrical Engineering, Statistics, or a related technical field.
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  5+ years of hands-on experience in machine learning and software engineering.
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  Demonstrated experience leading technical teams or complex ML projects in production environments.
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  Strong understanding of machine learning and AI concepts, including:
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    supervised and unsupervised learning,
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    classification,
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    regression,
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    clustering,
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    and deep learning techniques.
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  Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and Scikit-learn.
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  Strong SQL and cloud platform experience.
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  Hands-on experience working with time-series data.
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  Excellent communication and cross-functional collaboration skills.

Preferred Qualifications:

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  Master's or PhD in Computer Science, Electrical Engineering, Statistics or a related field.
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  Experience working in industrial or manufacturing environments.
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  Familiarity with MLOps tools and platforms such as MLflow, Airflow, Docker, and Kubernetes.
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  Experience with signal processing, edge computing or physics-informed machine learning models.