AI / ML Engineer - MLOps

Responsibilities:

• Design, develop, and manage scalable end-to-end machine learning pipelines.
• Deploy, monitor, and maintain ML models in production environments.
• Implement MLOps practices for model versioning, automation, testing, and continuous deployment.
• Optimize model performance, scalability, reliability, and resource utilization.
• Integrate Generative AI models and AI services into enterprise applications.
• Support AI/ML deployments across cloud and on-premises environments.
• Collaborate with cross-functional teams and provide technical solutions for complex issues.
• Mentor team members and contribute to technical decisions and best practices.

Requirements:

• Minimum 5 years of hands-on experience in machine learning operations.
• Experience building and managing production-grade ML pipelines.
• Strong understanding of model deployment, monitoring, automation, and lifecycle management.
• Knowledge of cloud platforms and production AI/ML systems.
• Ability to lead technical activities and collaborate with multiple teams.
• Skills: MLOps, machine learning, Python, ML pipelines, model deployment, model monitoring, CI/CD, Docker, Kubernetes.

Good-to-Have Skills:

• AWS / Azure / Google Cloud AI services.
• Docker and Kubernetes.
• MLflow, Kubeflow, or similar ML lifecycle tools.
• TensorFlow or PyTorch.
• Generative AI, LLMs, and RAG.
• Deep Learning and Neural Networks.

Must-Have Skills:

• Machine Learning Operations (MLOps).
• ML Pipeline Development and Automation.
• Model Deployment and Production Monitoring.
• Python.
• CI/CD for Machine Learning.
• Cloud-Based AI/ML Deployment.