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
Lead, coach, and support a high-performing squad of Machine Learning Engineers, fostering psychological safety, professional growth, and a culture of continuous delivery.
Drive the end-to-end technical direction and operational excellence for ML infrastructure, automated training, model evaluation, and low-latency inference pipelines.
Partner with Product Managers, Data Scientists, and Data Engineering leads to align roadmaps, scope technical initiatives, and deliver high-impact features predictably.
Define and champion engineering best practices across MLOps, system observability, data quality monitoring, and automated testing to ensure high reliability.
Provide architectural guidance and participate in technical design reviews, helping the team evaluate trade-offs between speed, scalability, and long-term maintainability.
Champion technical hiring and interview calibration to attract, evaluate, and onboard diverse engineering talent.
Experience & Background
Proven experience in technical leadership or engineering people management, guiding small-to-medium squads through complex software delivery cycles.
Strong software engineering background with hands-on experience deploying and operating machine learning pipelines in production environments (e.g., Python, Docker, Kubernetes).
Deep understanding of MLOps lifecycle tools, workflow orchestration, model serving, and cloud infrastructure (AWS or equivalent).
Demonstrated ability to build strong cross-functional alignment between engineering, data science, and product management.
Nice-to-havesExperience designing or scaling streaming ML systems handling high-frequency time-series data or spatial-temporal models.
Exposure to modern ML platforms, feature stores, and distributed compute frameworks.
Familiarity with commodity, maritime, energy, or financial market domains.
Skills Required
- Experience in technical leadership or engineering people management
- Experience guiding small-to-medium engineering squads through complex software delivery cycles
- Strong software engineering background
- Hands-on experience deploying and operating machine learning pipelines in production
- Experience with Python, Docker, and Kubernetes
- Deep understanding of MLOps lifecycle tools, workflow orchestration, model serving, and cloud infrastructure
- Experience with AWS or equivalent cloud infrastructure
- Ability to build cross-functional alignment between engineering, data science, and product management
- Experience designing or scaling streaming ML systems with high-frequency time-series data or spatial-temporal models
- Exposure to modern ML platforms, feature stores, and distributed compute frameworks
- Familiarity with commodity, maritime, energy, or financial market domains
What We Do
Kpler is the leading data & analytics firm providing real-time transparency in commodity markets. Relying on a methodology that combines artificial and human intelligence, the Kpler platform provides real-time data and analytics (global flows, storage, freight) on more than 40 commodities including crude oil, refined products, LNG, LPG, and dry bulk.
