DevMLops Engineer

Sunnyvale, CA - USA

Monthly Salary: Not Disclosed

Posted on: Yesterday

Vacancies: 1 Vacancy

Job Summary

Hello Very Very Urgent

Title :MLops Engineer

Duration: 12 months

Location: Sunnyvale CA ( 3 days onsite 2 days remote )

Required Skills:

Overview

We are seeking a skilled Machine Learning Engineer with strong MLOps capabilities to help design build and scale machine learning systems from development through production. This role focuses on operationalizing models building reliable infrastructure and supporting data scientists in deploying and maintaining machine learning solutions.

Key Responsibilities

Build maintain and scale MLOps pipelines including model training versioning validation deployment and monitoring.

Partner with data scientists to productionize machine learning models and ensure seamless deployment across environments.

Develop tools frameworks and platforms that improve visibility into model performance behavior and lifecycle.

Create and manage automated CI/CD workflows for ML assets ensuring repeatable and reliable model releases.

Implement observability best practices such as logging alerting performance tracking and drift detection.

Optimize infrastructure to support high-performance model execution and scalable experimentation.

Collaborate closely with engineering teams to integrate ML models into production systems.

Maintain documentation technical standards and best practices for ML engineering and deployment processes.

Qualifications

Required

Strong proficiency in end-to-end machine learning engineering including data preparation feature pipelines deployment and monitoring.

Hands-on experience with MLOps tools such as MLflow Kubeflow SageMaker Vertex AI or similar.

Backend or full-stack development experience with one or more languages (Python Java JavaScript/Node Go etc.).

Familiarity with cloud environments (AWS GCP Azure) and containerization (Docker Kubernetes).

Experience building automated CI/CD pipelines for ML workflows.

Strong understanding of model versioning reproducibility and experiment tracking.

Ability to work in a fast-paced environment and collaborate across data science engineering and product teams.

Thanks and Regards

Naveen

US IT Recruiter

Conch Technologies Inc

6750 Poplar Ave # 711 Memphis TN.

Direct:

Email:

Hello Very Very Urgent Title :MLops Engineer Duration: 12 months Location: Sunnyvale CA ( 3 days onsite 2 days remote ) Required Skills: Overview We are seeking a skilled Machine Learning Engineer with strong MLOps capabilities to help design build and scale machine learning s...

Hello Very Very Urgent

Title :MLops Engineer

Duration: 12 months

Location: Sunnyvale CA ( 3 days onsite 2 days remote )

Required Skills:

Overview

We are seeking a skilled Machine Learning Engineer with strong MLOps capabilities to help design build and scale machine learning systems from development through production. This role focuses on operationalizing models building reliable infrastructure and supporting data scientists in deploying and maintaining machine learning solutions.

Key Responsibilities

Build maintain and scale MLOps pipelines including model training versioning validation deployment and monitoring.

Partner with data scientists to productionize machine learning models and ensure seamless deployment across environments.

Develop tools frameworks and platforms that improve visibility into model performance behavior and lifecycle.

Create and manage automated CI/CD workflows for ML assets ensuring repeatable and reliable model releases.

Implement observability best practices such as logging alerting performance tracking and drift detection.

Optimize infrastructure to support high-performance model execution and scalable experimentation.

Collaborate closely with engineering teams to integrate ML models into production systems.

Maintain documentation technical standards and best practices for ML engineering and deployment processes.

Qualifications

Required

Strong proficiency in end-to-end machine learning engineering including data preparation feature pipelines deployment and monitoring.

Hands-on experience with MLOps tools such as MLflow Kubeflow SageMaker Vertex AI or similar.

Backend or full-stack development experience with one or more languages (Python Java JavaScript/Node Go etc.).

Familiarity with cloud environments (AWS GCP Azure) and containerization (Docker Kubernetes).

Experience building automated CI/CD pipelines for ML workflows.

Strong understanding of model versioning reproducibility and experiment tracking.

Ability to work in a fast-paced environment and collaborate across data science engineering and product teams.