Job Description • 3+ years of experience as a Machine Learning Engineer, showcasing a strong grasp of machine learning principles alongside data engineering concepts. • Experience in Optimization using any commercial or open-source problem solvers • Strong programming skills in languages such as Python or R, along with experience in using machine learning libraries and frameworks like TensorFlow, PyTorch, or Scikit-learn for model development and evaluation. • Strong understanding of machine learning algorithms and techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning, to effectively implement and optimize models in production environments. • Proficiency in cloud platforms such as Azure or Google Cloud Platform (GCP), with hands-on experience in utilizing their machine learning services and tools for model deployment and management. • Experience with orchestration tools like Kubeflow and Apache Airflow for managing machine learning workflows and data pipelines. • Experience with containerization technologies like Docker and orchestration tools such as Kubernetes, enabling efficient deployment, scaling, and management of machine learning applications. • Knowledge of CI/CD practices and tools, including Jenkins, GitLab or GitHub to automate the deployment and testing of machine learning models, ensuring rapid and reliable updates. • Understanding of monitoring and logging tools (e.g., Prometheus, or Grafana) to track model performance and system health, allowing for proactive management and troubleshooting. • Familiarity with data processing frameworks such as Pandas, Dask, and Spark, which are essential for handling large datasets and performing distributed data processing in cloud environments. • Excellent problem-solving skills and the ability to work collaboratively in a team environment, adapting to new challenges and technologies as the field of machine learning continues to evolve. Compensation: $20-30/hour Exact compensation may vary based on several factors, including skills, experience, and education. 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/. Skills and Requirements - 3+ years as a Machine Learning Engineer with a strong grasp of machine learning principles and data engineering concepts - Experience using cloud ML services and tools for deployment and management - Strong programming skills in Python or R - Experience with ML libraries/frameworks: TensorFlow, PyTorch, Scikit‑learn - Ability to implement and optimize models in production environments - Proficiency in Azure or Google Cloud Platform (GCP) - Experience with orchestration tools: Kubeflow and/or Apache Airflow - Experience with Containerization technologies: Docker and Kubernetes - Knowledge of CI/CD Practices and tools such as Jenkins, GitLab and Github