MLOps / AI Ops Engineer

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

This role is focused on the successful completion of specific tasks and deliverables. Your responsibilities will include:

• 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

We are looking for someone with a proven track record of successful contract engagements. The ideal candidate will have:

• 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.

Pay

• $50/hr – $175/hr

Schedule

• Contract Length: 12 Months
• Start Date: ASAP