Senior ML Infra Engineer — Data Pipelines & Production

Xora Innovation; Singapore; Hybrid; SGD 205,000 - 281,000; Full time

Job summary

ELEMYNT is seeking a Senior ML Infrastructure Engineer to build and operate the data and ML infrastructure that powers our platform. You will own both data pipelines and model deployment, ensuring fast, scalable, and observable systems across customer environments.

In this hands-on role, you will implement CI/CD, model registries, monitoring, and dashboards, collaborating with data scientists and engineers in an early-stage startup setting to keep systems reliable and observable.

Qualifications

• Build and shipping production software across data and ML infra.
• Strong Python and end-to-end system reliability.
• Experience with large-scale data systems and distributed engines.
• Production MLOps: packaging, serving, monitoring, CI/CD.
• Containers and orchestration; cloud and HPC with multi-GPU.

Responsibilities

• Design and operate data pipelines ingesting large-scale scientific output.
• Develop ML data pipelines for training, fine-tuning, and RL.
• Implement validation gates to ensure data quality before training.
• Package, version, and deploy models with registries and reproducible builds.
• Run CI/CD and serving workflows for batch, online, and asynchronous inference.
• Monitor drift, latency, and anomalies; set up dashboards and alerts.
• Design APIs, services, and internal tools for reliability and usability.
• Ensure observability in on-prem and cloud environments.

Skills

Python

Large-scale data systems

Production MLOps

Docker

Kubernetes

Airflow

Dagster

Flyte

Temporal

Prometheus

Grafana

OpenTelemetry

Cloud computing

HPC

Multi-GPU

Education

Bachelor's or Master's in Computer Science or related engineering

Tools

Dagster

Flyte

Kubeflow

MLflow

Weights & Biases

Ray Serve

KServe

Kubeflow

Job description

ELEMYNT is seeking a Senior ML Infrastructure Engineer to build and operate the data and ML infrastructure that powers our platform. You will own both data pipelines and model deployment, ensuring fast, scalable, and observable systems across customer environments.

In this hands-on role, you will implement CI/CD, model registries, monitoring, and dashboards, collaborating with data scientists and engineers in an early-stage startup setting to keep systems reliable and observable.