Sobre este puesto de Machine Learning Engineering Manager (Power) en Kpler
About the Role
As a Machine Learning Engineering Manager at Kpler, you will lead and grow an engineering team dedicated to bringing production-grade machine learning models to the core of global trade intelligence. Operating at the intersection of people leadership, system architecture, and applied AI, you will guide your team in scaling resilient MLOps pipelines and deploying models that process massive, real-time datasets. Your leadership directly accelerates how our platform translates complex market telemetry into actionable, high-impact signals for global commodity and maritime leaders.
Key Responsibilities
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Lead, coach, and support a high-performing squad of Machine Learning Engineers, fostering psychological safety, professional growth, and a culture of continuous delivery.
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Drive the end-to-end technical direction and operational excellence for ML infrastructure, automated training, model evaluation, and low-latency inference pipelines.
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Partner with Product Managers, Data Scientists, and Data Engineering leads to align roadmaps, scope technical initiatives, and deliver high-impact features predictably.
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Define and champion engineering best practices across MLOps, system observability, data quality monitoring, and automated testing to ensure high reliability.
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Provide architectural guidance and participate in technical design reviews, helping the team evaluate trade-offs between speed, scalability, and long-term maintainability.
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Champion technical hiring and interview calibration to attract, evaluate, and onboard diverse engineering talent.
Experience & Background
What you'll need (Must-haves)
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Proven experience in technical leadership or engineering people management, guiding small-to-medium squads through complex software delivery cycles.
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Strong software engineering background with hands-on experience deploying and operating machine learning pipelines in production environments (e.g., Python, Docker, Kubernetes).
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Deep understanding of MLOps lifecycle tools, workflow orchestration, model serving, and cloud infrastructure (AWS or equivalent).
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Demonstrated ability to build strong cross-functional alignment between engineering, data science, and product management.
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Experience designing or scaling streaming ML systems handling high-frequency time-series data or spatial-temporal models.
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Exposure to modern ML platforms, feature stores, and distributed compute frameworks.
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Familiarity with commodity, maritime, energy, or financial market domains.
