ML Ops Engineer

We’re seeking an experienced ML Ops Engineer to operationalize machine learning models developed by our data science team. This role is critical in bridging the gap between model development and scalable, production-ready deployment.


Key Responsibilities:
Deploy and maintain machine learning models in production environments.
Build automated pipelines for model retraining and monitoring.
Collaborate closely with data scientists to transform model code into robust services.
Develop APIs and services to make models accessible across the organization.
Manage infrastructure using Docker, Kubernetes, and CI/CD tools.
Handle data pipelines, feature engineering, and visualization tasks.
Ensure Linux-based systems are optimized for ML workloads.

Based on knowledge and years of experience, this position offers an hourly pay rate of $65-70/hr

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

Strong background in Python and SQL for data-related tasks.
Experience with ML frameworks such as PyTorch, Scikit-learn, and TensorFlow.
Solid understanding of machine learning engineering and MLOps principles.
Proficiency in infrastructure tools: Docker, Kubernetes, CI/CD.
Ability to create scalable APIs and services.
Deep familiarity with Linux environments.
Real-world experience deploying ML systems in production across industries.

Experience with cloud platforms (Azure ML, AWS, GCP).
Knowledge of GPUs/CPUs for model optimization.
Strong grasp of math, algorithms, and data science concepts.
Understanding of ML terminology and lifecycle.

Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.