Architect- Machine Learning Platform Engineer

Overview Job Summary: As a Machine Learning Engineer with expertise in DevOps and Kubernetes, 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. Responsibilities Key Responsibilities: 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 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. 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. Collaboration and Communication: 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. Qualifications Qualifications: Technical Skills: 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. Handson experience with Istio Service Mesh Hands-on experience with cloud platforms(Azure, AWS, GCP etc.) Experience: 5+ years of experience in machine learning engineering. 4+ years of experience in DevOps practices. Proven experience in deploying and managing ML models in production environments using Kubernetes. Soft Skills: Excellent problem-solving and analytical skills. Strong communication and teamwork abilities. Ability to work in a fast-paced and dynamic environment. Preferred Qualifications: 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.