Description

You will architect, deploy, and optimize enterprise-level machine learning and MLOps solutions.

Responsibilities

• Design and implement scalable ML solutions from data ingestion through model deployment.
• Guide data scientists on algorithm selection, model explainability, and computational efficiency.
• Develop CI/CD pipelines for ML models using tools like MLflow or Kubeflow.
• Implement model versioning, drift detection, and automated retraining protocols.
• Architect solutions for Generative AI, LLMs, and reinforcement learning applications.

Required Skills

• 8+ years in AI/ML, including 3+ years as an ML or MLOps Architect.
• Strong expertise in Python, Scala, or Java.
• Proficiency in TensorFlow, PyTorch, XGBoost, or SciKit-learn.
• Expertise in MLflow, Kubeflow, SageMaker, or Azure ML.
• Hands-on experience with AWS, GCP, or Azure cloud environments.
• Knowledge of Apache Spark or Kafka for large-scale ML processing.
• Experience with Docker and Kubernetes for containerization.
• Familiarity with CI/CD principles and infrastructure automation.

Key Skills

Education

Any Graduate