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Strong brand, metro location, mid-level experience, and broad MLOps tooling amplify candidate competition.
MLOps skills transfer across industries, but enterprise-scale telecom and large-data platform experience moderately favors domain-fit candidates.
Explicit 3-year minimum plus extensive production MLOps, cloud, and tooling requirements drive strict screening.
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Design, build, and manage end-to-end MLOps infrastructure that enables reliable, scalable, and secure production deployment of machine learning models.
Deploy, operationalise, monitor, and optimise machine learning models and feature engineering pipelines ensuring high availability, performance, and continuous improvement.
Collaborate with cross-functional teams to integrate AI/ML solutions into enterprise applications following best practices in CI/CD, security, governance, and scalability.
Bachelor's or Master's degree in Computer Science, Engineering, or related discipline.
Minimum 3 years of experience in MLOps, Machine Learning Engineering, Data Engineering, or relevant software engineering role supporting production environments.
Strong proficiency in Python and SQL; experience with Java, Scala, Apache Spark, and cloud platforms (AWS, GCP, Azure).
Experience with MLOps tools and platforms such as GitLab, Jenkins, Airflow, MLflow, Hadoop, Docker, Kubernetes, and AI/ML frameworks.
Experienced in transforming experimental ML models into secure, maintainable, production-grade services at scale within enterprise environments.
Skilled in operating large-scale distributed data processing systems using Spark, Hadoop ecosystems, and cloud-native technologies.
Comfortable working in cross-disciplinary teams bridging data science, software engineering, platform engineering, and business functions with focus on operational excellence.