Senior AI/ML Engineer – Embedded Edge Systems & Intelligent Power Management

Company Description

 Kenetics Energy Pte. Ltd. is the energy solutions division of Kenetics Innovations, a technology group with a 25-year track record of delivering nationwide, mission-critical systems for Singapore’s transit payment ecosystem. Combining cutting-edge systems with premium engineering expertise, we design, optimize, and scale smart power architectures globally. Our mission is to transform off-grid renewable energy by delivering stable, reliable power to remote and rural environments. By resolving systemic issues like inconsistent supply and poor remote manageability, Kenetics Energy ensures dependable power access for both daily community living and mission-critical applications worldwide.

Role Description

The Senior AI/ML Engineer – Embedded Edge Systems & Intelligent Power Management is a full-time, on-site role based in Singapore. This position is responsible for designing, developing, and optimizing AI/ML models that run on embedded edge devices to improve renewable energy system performance and reliability.

• New feature development of edge PCB intelligence & computer vision,
• New feature development of Smart Battery fleet management system(BMS) analytics
• New feature development of predictive & dynamics power control

Teamwork, learning & knowledge sharing spirit

Because this role sits at the intersection of evolving hardware capabilities and cutting-edge machine learning methodologies, technical skill alone is not enough. We require a collaborative engineering anchor who excels in an interactive, face-to-face team environment:

• Proactive Self-Learning & Research Mastery: You demonstrate a continuous appetite for staying ahead of the AI curve. You possess the academic literacy to independently source, read, and critique deep technical research articles and SCI index papers. You excel at translating advanced theoretical findings directly into production-grade features.

• Cross-Functional Group Discussion: You act as a technical bridge. Working on-site, you will regularly engage in spontaneous and scheduled cross-functional design sessions with hardware PCB designers, firmware specialists, and cloud engineers to whiteboard problems, debate architecture trade-offs, and solve complex system-level bottlenecks.

• Knowledge Democratization: You actively lift the capabilities of the engineering organization. This includes documenting complex data pipelines, conducting code reviews, and leading internal technical brown-bag sessions or workshops to share your findings on edge optimization, literature reviews, and engineering techniques.

• Collaborative Mindset: You value collective success over individual praise. You actively pair-program when a teammate is blocked, welcome diverse perspectives during architecture reviews, and maintain an approachable, supportive presence within the engineering sprint.

Technical requirement & qualifications

• Experience: 5+ years of professional experience as an AI/ML Engineer, with a clear track record of shipping machine learning models into production software features that interact directly with physical hardware, batteries, or IoT fleets.

• Literature & Research Literacy: Proven ability to read, interpret, and implement methodologies directly from peer-reviewed scientific journals (IEEE, Elsevier, SCI-indexed journals) focused on ML, IoT, or battery physics.

• Core ML Frameworks: Deep proficiency with PyTorch, TensorFlow, or JAX; expert familiarity with Scikit-learn and advanced time-series analysis toolkits (e.g., Prophet, XGBoost, LSTM architectures) for battery degradation modeling.

• Embedded & Edge Optimization: Verifiable experience optimizing models for resource-constrained edge computing PCBs and microcontrollers (ARM Cortex-M/A, NVIDIA Jetson, or specialized TPUs). Mastery of tools like TensorRT, ONNX Runtime Mobile, TensorFlow Lite, or TinyML frameworks.

• Programming Mastery: Expert-level Python skills paired with a solid working knowledge of C/C++ for integrating model outputs or running inference engines directly inside embedded environments.

• Data & Fleet Pipelines: Strong competency managing high-frequency, streaming time-series data pipelines (MQTT, Kafka, TimescaleDB, or InfluxDB) sent from thousands of distributed hardware nodes to a centralized cloud interface.

• Workplace Preference: Strong preference for full-time on-site work and close collaboration with lab hardware, electrical test equipment, and engineering peers.

Additional Information

Join an international, collaborative team working on diverse, high-impact energy projects. We offer a competitive salary, performance bonuses, and strong benefits.

To help drive our business and contribute to the energy transition, please apply with your CV and a cover letter stating your motivation for joining KENETICS ENERGY and your salary expectations.