Senior ML Ops Engineer: Productionize LLMs at Scale

voltai-com; Edison (CA); On-site; USD 150,000 - 230,000; Full time

Benefits offered by this job

Unlimited PTO

Comprehensive Health Coverage

Free Meals and Snacks

Professional Growth

Visa Sponsorship

Job summary

Voltai is seeking engineers to design, build, and operate scalable ML pipelines for training, evaluation, and deployment of LLMs and retrieval-augmented systems. You will automate the ML lifecycle, monitor model quality, and optimize performance across diverse hardware and cloud environments.

Join a team blending software and hardware expertise to productionize foundation models for enterprise customers, while balancing latency, cost, and security in fast-paced settings.

Qualifications

• Experience building reliable, scalable ML systems.
• End-to-end ML lifecycle management experience.
• Ability to translate research into production-ready systems.

Responsibilities

• Design, build, and maintain scalable ML pipelines for training, evaluation, and deployment of LLMs.
• Operationalize evaluation workflows using synthetic and human-labeled data across deployments.
• Automate the ML Developer lifecycle with data versioning and CI/CD pipelines.
• Optimize model training and inference for latency, throughput, and cost.
• Collaborate cross-functionally to productionize foundation models.
• Deploy and manage both open-source and proprietary models with latency, security, and compliance constraints.
• Implement real-time monitoring to detect drift and bottlenecks in live systems.
• Work directly with enterprise customers on deployment strategies and feedback loops.

Skills

Python

Go

Rust

ML Ops

System design

Cross-functional

Performance

Tools

MLflow

Kubeflow

SageMaker

Vertex AI

Apache Airflow

Docker

Kubernetes

Pulumi

Terraform

Weights & Biases

CometML

Job description

Voltai is seeking engineers to design, build, and operate scalable ML pipelines for training, evaluation, and deployment of LLMs and retrieval-augmented systems. You will automate the ML lifecycle, monitor model quality, and optimize performance across diverse hardware and cloud environments.

Join a team blending software and hardware expertise to productionize foundation models for enterprise customers, while balancing latency, cost, and security in fast-paced settings.