Senior ML Ops Engineer: Cloud Pipelines & AutoML

Mphasis; Concord (CA); On-site; USD 120,000 - 170,000; Full time

Job summary

Tachyon Cortex is seeking an ML Ops Engineer to drive end-to-end ML solution lifecycle. You will design and maintain pipelines using MLflow, Kubeflow, or Vertex AI and collaborate with cross-functional teams.

The role emphasizes cloud deployment, CI/CD for models, monitoring, governance, and low-latency scoring. This position requires strong software engineering with DevOps practices and the ability to communicate complex concepts.

Qualifications

• 10+ years of software engineering experience with 3+ years in AIML and MLOps.
• Experience building ML pipelines and model deployment.
• Strong communication to non-technical stakeholders.

Responsibilities

• Develop and maintain ML pipelines with MLflow, Kubeflow, or Vertex AI.
• Automate training, testing, deployment, and monitoring in the cloud.
• Implement CI/CD workflows for model lifecycle management.
• Monitor model performance and ensure governance and explainability.
• Collaborate to provision containerized environments and support scoring via low-latency APIs.
• Leverage AutoML tools for low-code model development and rapid deployment.

Skills

Java

Python

SQL

ML libraries

Docker

Kubernetes

Airflow

Spark

DevOps

Communication

Tools

Docker

Kubernetes

Airflow

Spark

Job description

Tachyon Cortex is seeking an ML Ops Engineer to drive end-to-end ML solution lifecycle. You will design and maintain pipelines using MLflow, Kubeflow, or Vertex AI and collaborate with cross-functional teams.

The role emphasizes cloud deployment, CI/CD for models, monitoring, governance, and low-latency scoring. This position requires strong software engineering with DevOps practices and the ability to communicate complex concepts.