Job Description & Responsibilities

Develop robust and scalable ML pipelines using tools like Kubeflow or custom solutions.
Implement data version control (DVC) for tracking changes in datasets.
Optimize data processing steps, feature engineering, and model training.
Model Deployment and Monitoring:
Deploy ML models to production environments (on-premises or cloud).
Set up monitoring for model performance, data drift, and concept drift.
Implement model-serving frameworks (e.g., TensorFlow Serving, TorchServe).
Leverage vector databases (e.g., Faiss, Milvus) for efficient storage and retrieval of high-dimensional embeddings.
Optimize similarity search for recommendation systems, content matching, and clustering.
Proficiency in Python and PyTorch for model development and experimentation.
Familiarity with TensorFlow for deep learning workflows.
Write clean, maintainable, and efficient code.
Design infrastructure for distributed ML training and inference.
Optimize resource allocation (CPU, GPU, memory) for scalability.
Work with containerization (Docker, Kubernetes) and orchestration tools.
Implement GitOps practices for model versioning and deployment.
Define CI/CD pipelines for automated testing, validation, and deployment.
Ensure reproducibility and traceability.
Mentor junior team members and guide them in best practices.
Collaborate with data scientists, engineers, and DevOps teams.
Stay updated with industry trends and emerging technologies.

ML Ops Architect|Multiple|6+Yrs

Job Description & Responsibilities