ML Engineer-Intl: India

- You will be joining a fortune 100 client out of their Hyderabad, India office, as a Machine Learning Engineer on the Platform team. This team is primarily a DevOps team looking after Kubernetes containers and has since rebranded into an AI/ML platform team. They are needing an ML engineer that is able to deploy ML models into Kubernetes. As a Machine Learning Engineer, you will be responsible for designing, implementing, and maintaining scalable ML infrastructure. You will work closely with data scientists, software engineers, and IT teams to ensure seamless integration and deployment of ML models into production environments. You will also be responsible for optimizing workflows and ensuring the scalability and reliability of our systems. Key responsibilities include, but not limited to: o ML Model Development: Collaborate with data scientists to understand model requirements and provide technical guidance on model optimization and deployment. Develop, test, and deploy machine learning models using appropriate frameworks and libraries. Research the industry's latest machine learning platform technologies and create quick prototypes / proof-of-concepts. Closely work with cross-functional partner teams in global settings to deliver new ML features and solutions and achieve business objectives. Solid theoretical background in machine learning or data mining and strong conceptual, problem-solving, and analytical skills o DevOps & IAC: Implement CI/CD pipelines for ML workflows to automate model training, testing, and deployment. Ensure robust version control and manage model lifecycle using tools like Git, Jenkins, and Docker. Having extensive industry experience with Infrastructure such as Code (Terraform), orchestration tools ( Airflow, AWS Step/Lambda), building CI/CD pipelines using GitHub Actions, and real-time monitoring/alerting frameworks such as Prometheus and Grafana. Build and maintain cloud infrastructure (e. g., AWS, GCP, Azure) to support ML operations. Monitor and optimize system performance, ensuring cost-efficiency and scalability. Implement security best practices to safeguard data and models. o Kubernetes Orchestration: Design and manage Kubernetes clusters for deploying scalable ML models and applications. Implement Kubernetes Operators for managing ML workflows and resources. Optimize resource utilization and ensure high availability and reliability of ML services on Kubernetes. o Collaboration: Work with cross-functional teams to integrate ML models into business applications. Provide technical support and training to team members on ML and DevOps practices. Document processes, workflows, and infrastructure setups for transparency and knowledge sharing.

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/ . - Experience with MLOps tools such as Kubeflow, MLflow, or TFX - Familiarity with monitoring tools such as Prometheus and Grafana - Knowledge of infrastructure-as-code tools like Terraform or Ansible - Understanding of data engineering concepts and tools - 5+ years of experience in machine learning engineering with of the time being within DevOps environment - Proven experience in deploying and managing ML models in production environments using Kubernetes - Strong programming skills in Python, with experience in ML libraries such as TensorFlow, PyTorch, or scikit-learn - Proficient in DevOps tools and practices including Docker, Jenkins, and Git - Extensive experience with Kubernetes for container orchestration and management - Hands-on experience with cloud platforms (AWS, GCP, or Azure) - Excellent problem-solving and analytical skills; strong communication and teamwork abilities; ability to work in a fast-paced and dynamic environment - Bachelors or Masters degree in Computer Science, Engineering, or a related field