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Entry-level ML/MLOps with broad skills and a recognizable multinational brand increases applicant competition.
MLOps and ML engineering skills are transferable but require technical ML domain knowledge.
Multiple mandatory technical skills despite junior experience requirement increase filtering rigor.
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Maintain infrastructure and tools to deploy and manage machine learning models at scale.
Develop and maintain data engineering pipelines and CI/CD pipelines for machine learning models.
Collaborate with data scientists to integrate MLOps best practices and reduce deployment cycle time.
Bachelor's degree in computer science or equivalent software engineering discipline.
0-2 years of relevant experience (including freshers with understanding in machine learning).
Knowledge of machine learning frameworks (TensorFlow, PyTorch, or Scikit-learn), cloud platforms (AWS, Azure, or GCP), CI/CD pipelines, containerization (Docker, Kubernetes).
Understanding of software engineering best practices including version control, testing, and deployment.
Early-career professional with foundational understanding of machine learning, software engineering, and cloud infrastructure.
Ability to manage multiple projects and priorities in a fast-paced environment while collaborating across teams.
Experience or interest in data engineering, DevOps, and Agile environments is advantageous but not strictly required.