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Broad MLOps and ML skillset requirements for a fresher increase applicant density.
ML/MLOps skills are specialized yet transferable across industries.
Multiple technical must-haves (ML frameworks, cloud, CI/CD) but no explicit years creates moderate filtering.
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Develop and maintain infrastructure and tools to deploy machine learning models at scale.
Build and maintain data engineering and CI/CD pipelines for ML models.
Collaborate with data scientists and other teams to integrate MLOps best practices and deliver business features.
Bachelor's degree in Computer Science, Software Engineering, or related field.
Fresher with understanding of machine learning frameworks (TensorFlow, PyTorch, or Scikit-learn).
Knowledge of cloud platforms (AWS, Azure, or GCP) and containerization technologies (Docker, Kubernetes).
Understanding of data engineering pipelines, CI/CD, and ML algorithms (regression, classification, clustering, deep learning).
Comfortable working across ML model development, software engineering, and cloud infrastructure.
Capable of managing multiple projects in a fast-paced environment with cross-team collaboration.
Proactive learner who can apply academic research and new data science techniques independently over time.