Data & ML Platform Engineer

About the Role L3 Business Group is growing its R&D software team and is looking for a Data & ML Platform Engineer to own the data infrastructure and machine learning platform that sits at the core of our AI-powered energy intelligence system. You will be responsible for building and maintaining the data engineering foundation that makes our AI platform work — designing the data schemas, standing up the model registry, enforcing the data pipeline boundary between edge devices and the cloud, and progressively building the machine learning infrastructure as data accumulates. The role evolves toward owning the ML pipeline that continuously improves our AI models as more real-world data flows in. You will work directly with our System Architect, the Embedded Software Engineer, and our international R&D research partners in a small, technically serious team where your work has direct and measurable impact from day one. Key Responsibilities Design and implement the canonical data schema — defining how every data point is structured, tagged, and stored with full provenance (source, timestamp, ownership assertion, consent basis); this foundation underpins all downstream AI and analytics capabilities Build and maintain a versioned model registry — every AI model version must be tagged with a full retraining audit trail and drift detection log, ensuring the platform improves systematically over time Build and enforce the data pipeline boundary between edge controllers and the cloud — the API layer serves structured inference outputs only, never raw sensor data Design and implement the cloud-side data ingestion pipeline from edge controllers, working closely with the Embedded Software Engineer on the controller-to-cloud interface Establish data provenance records per ingestion event — critical for IP documentation and legal defensibility Build the cloud platform infrastructure — databases, schemas, data storage, and processing pipelines — that the broader AI system depends on Progressively build and own the continuous learning pipeline that retrains and improves AI models as real-world data accumulates — evolving from data infrastructure toward full ML platform ownership as the dataset matures Implement drift detection and model performance monitoring, flagging when deployed models require retraining and managing the versioning, rollback, and redeployment process Design and maintain the API layer that serves AI model outputs to downstream systems and customer-facing platforms — working with the broader engineering team on interface requirements Ensure all data handling meets relevant data governance and privacy requirements, with clear documentation throughout Requirements Minimum 2–3 years of hands-on experience in data engineering, ML platform engineering, or a closely related role on real production systems; A strong fresh graduate with demonstrable data engineering or ML project experience will be considered. Strong proficiency in Python — primary language for data pipelines and ML infrastructure Experience designing and implementing data schemas and relational or time-series databases (PostgreSQL, TimescaleDB, InfluxDB, or similar) Experience building and managing data pipelines (Airflow, Prefect, or equivalent) Familiarity with model registry concepts and ML lifecycle management (MLflow or similar) Experience with cloud infrastructure — AWS, GCP, or Azure Understanding of API design — RESTful or GraphQL — and experience building clean data-serving APIs Strong documentation discipline — this role produces formal data provenance records and schema documentation, not just code Able to work independently and collaboratively in an R&D environment with international research partners Singapore Citizen or Permanent Resident preferred Good to have Experience with time-series data — sensor data, IoT telemetry, or energy data specifically Familiarity with streaming data platforms (Kafka, MQTT, or similar) Experience with drift detection, model monitoring, or MLOps practices Exposure to edge-to-cloud data architectures or IoT data pipelines. Understanding of data privacy, data provenance, or data governance frameworks Career Growth You will work directly with our System Architect throughout the initial build phase, with full ownership of the data and ML infrastructure from day one. As the platform and dataset grow, the role evolves naturally from data engineering toward ML platform ownership — you will be the person who decides h…