Production-Ready ML Infrastructure Engineer

Xora Innovation; San Diego (CA); Hybrid; USD 160,000 - 260,000; Full time

Benefits offered by this job

On-site or hybrid work model

Job summary

Elemynt, built by Xora Innovation, seeks an experienced engineer to own data and ML infrastructure for scalable, production-grade AI workflows. You will craft pipelines, validate data quality, and ensure reliable model delivery across customer environments.

You will work across data ingestion, transformation, and model serving, with CI/CD, observability, and governance baked in from development to production.

Qualifications

• Bachelor's or Master's degree in Computer Science or a related engineering field
• 6+ years building and shipping production software with data systems and ML infrastructure
• Strong Python and experience shipping reliable end-to-end systems
• Experience with large-scale data systems and ML data pipelines

Responsibilities

• Build and operate data pipelines that ingest, transform, and curate large-scale scientific output into training-ready formats
• Package, version, deploy, and monitor models across development, staging, and production with traceable builds
• Run CI/CD and serving workflows for batch, online, and asynchronous inference with safe rollout and quick diagnosis
• Monitor models for drift, latency, anomalies, and surface issues via dashboards, logs, and alerts
• Design APIs, services, and internal tools to make workflows reliable and easy for engineers and scientists to use

Skills

Python

ML infrastructure

Data systems

Production software

Education

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

Tools

Great Expectations

Evidently

MLflow

Weights & Biases

Ray Serve

KServe

Kubeflow

Job description

Elemynt, built by Xora Innovation, seeks an experienced engineer to own data and ML infrastructure for scalable, production-grade AI workflows. You will craft pipelines, validate data quality, and ensure reliable model delivery across customer environments.

You will work across data ingestion, transformation, and model serving, with CI/CD, observability, and governance baked in from development to production.