About the role
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.