About
Architect & implement end-to-end AI pipelines, ensuring robust data ingestion, model training, and deployment Design scalable infrastructure using containers (Docker, Kubernetes) and cloud platforms (AWS, GCP, or Azure) Automate CI/CD pipelines for efficient testing, deployment, and monitoring of ML models
Qualifications
Bachelor's or Master's in Computer Science, Engineering, or related field Proficiency with ML frameworks (TensorFlow, PyTorch, Scikit-learn) and programming languages like Python or Golang Experience with MLOps tools (Kubeflow, MLflow, Jenkins) and version control (Git) Familiarity with big data (Spark, Hadoop, Kafka) and both SQL/NoSQL databases
Languages
- English
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