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Entry-level ML/MLOps in a metro with broad required skills yields moderate competition.
MLOps, cloud, and data engineering skills are broadly transferable across industries.
Explicit 0-2 years plus mandatory ML, cloud, CI/CD, and MLOps tooling creates moderate hiring filters.
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Develop and maintain infrastructure and tools for deploying and managing machine learning models at scale.
Build and sustain data engineering pipelines and CI/CD pipelines for machine learning workflows.
Collaborate with data scientists and other teams to integrate MLOps best practices and deliver business-requested features.
Bachelor's degree in computer science or equivalent software engineering discipline.
0-2 years of work experience (including freshers).
Understanding of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) and algorithms (regression, classification, clustering, deep learning).
Familiarity with cloud infrastructure (AWS, Azure, or GCP), CI/CD pipelines, containerization technologies (Docker, Kubernetes), and software engineering best practices.
Early career professional or fresher with foundational knowledge in machine learning and software engineering.
Capable of working across data engineering, MLOps, and collaborative team environments to reduce cycle times for ML solutions.
Comfortable managing multiple projects and priorities within a fast-paced, cross-functional team setting.