ML Ops Engineer

evrecruit.io•Columbus, OH

3d•$150,000•Hybrid

About The Position

We are seeking a skilled Senior AI/ML Ops Engineer to support and enhance our existing production AI applications and tools. This role focuses on maintaining, scaling, and optimizing deployed AI solutions rather than building entirely new models from scratch. You will work to ensure reliable, high-performance AI systems in a collaborative environment, automating processes and enabling continuous improvement through production feedback.

Requirements

Minimum 5+ years of hands-on software development experience (7+ preferred).
Bachelor's degree in Computer Science, Information Systems, or equivalent practical experience.
Strong proficiency in Python for prototyping, scripting, and deployment.
Solid experience with Azure cloud platform (additional AWS exposure a plus).
Hands-on experience with containerization (Docker, Kubernetes) and orchestration.
Familiarity with ML frameworks, libraries, and deployment practices (MLOps tools).
Understanding of NLP, generative AI, and production ML challenges.
Excellent communication skills to explain technical concepts to diverse audiences.
Self-motivated, collaborative, and enthusiastic about real-world AI applications.

Nice To Haves

Experience with fine-tuning or adapting large language models.
Additional programming (e.g., Java or similar).
Knowledge of reinforcement learning or advanced ML techniques.

Responsibilities

Automate deployment, monitoring, and scaling of AI/ML models in cloud environments (primarily Azure).
Maintain and troubleshoot production AI platforms, including break/fix support.
Build and manage CI/CD pipelines that handle data, code, and model updates.
Monitor model performance, drift detection, and implement updates or retraining as needed.
Collaborate with cross-functional teams (data scientists, engineers, DevOps) to integrate AI into workflows.
Support use cases such as natural language processing, sentiment analysis, recommendation systems, chatbots, and image-related tasks.
Develop synthetic data pipelines and leverage production signals for ongoing model refinement.
Potentially create prototypes or fine-tuned models to demonstrate enhancements.
Ensure security, compliance, and reliability of ML systems.
Provide occasional 24/7 support as part of a team rotation.