MLOps / AI Ops Engineer

DeWinter Group; Campbell (CA); Remote; USD 68,880 - 241,080; Full time

Job summary

A leading AI solutions provider is seeking an experienced MLOps / AI Ops Engineer for a remote 12-month contract. The role involves building and automating CI/CD pipelines for machine learning models, establishing monitoring frameworks, and managing deployment strategies. Candidates should have over 4 years of MLOps or DevOps experience with a strong focus on machine learning, alongside expertise in tools like MLflow and Kubeflow. Strong communication skills and the ability to work independently are essential for success in this impactful position.

Qualifications

• Minimum 4 years of experience in MLOps or DevOps focused on machine learning.
• Deep expertise in MLflow, Kubeflow, and CI/CD strategies.
• Ability to work autonomously and manage time effectively.

Responsibilities

• Build and automate end-to-end CI/CD pipelines for machine learning models.
• Set up observability frameworks to track model performance.
• Manage deployment strategies for zero-downtime updates.
• Automate scaling of inference services based on demand.
• Establish workflows for model governance and reproducibility.

Skills

MLOps expertise

DevOps knowledge

CI/CD tools proficiency (GitHub Actions, Jenkins)

Python programming

Kubernetes experience

Cloud-native ML services

Strong communication skills

Tools

MLflow

Kubeflow

Job description

Title: MLOps / AI Ops Engineer
Job Type: Contract
Contract Length: 12 Months
Pay Range: $50/hr – $175/hr
Start Date: ASAP
Location: Remote

About the Opportunity:
Our client, a leader in AI testing and Generative AI solutions, is looking for a skilled MLOps / AI Ops Engineer to join their team for a 12-month engagement. This project involves building and automating end-to-end CI/CD pipelines for machine learning models and establishing production observability frameworks to ensure model reliability and scalability. This is a high-impact role that requires a self‑motivated professional who can hit the ground running and deliver results quickly.

Key Responsibilities & Deliverables

• Building and automating end-to-end CI/CD pipelines for machine learning models.


• Setting up observability and monitoring frameworks to track model accuracy, latency, and drift in real‑time.


• Managing model deployment strategies (canary, A/B testing) to ensure zero‑downtime updates.


• Automating the scaling of inference services based on incoming request volume.


• Establishing standard workflows for model governance and reproducibility.

Required Skills & Experience

• 4+ years of experience in MLOps or DevOps with an ML focus.


• Deep expertise in MLflow, Kubeflow, and CI/CD tools (GitHub Actions, Jenkins). This isn't a learning role—you need to be a subject matter expert.


• Demonstrated ability to work autonomously and manage your own time effectively to meet project goals.


• Experience with Python, Kubernetes, and cloud‑native ML services.


• Strong communication skills to provide clear and concise status updates to the project team.