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Job Description
Structured overview of role & requirementsAbout This Role
Develop and maintain end-to-end machine learning pipelines including data processing, model training, deployment, and monitoring.
Support and operate model-serving infrastructure for batch and real-time inference workloads using containerized and cloud-native technologies.
Build and enhance CI/CD automation, testing, version control, and observability to improve reliability and scalability of production ML systems.
Minimum Requirements
Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, or a related field.
1-4 years of experience in machine learning engineering, MLOps, platform engineering, or related technical roles.
Strong Python programming skills with sound software engineering fundamentals including testing and version control.
Experience with Docker, Kubernetes, cloud platforms, CI/CD tools, and familiarity with ML lifecycle workflows such as training, evaluation, deployment, and monitoring.
Ideal Candidate Profile
Experienced in building scalable and reliable ML platforms and pipelines in collaborative, multi-disciplinary teams.
Familiar with MLOps tools and technologies like Airflow, MLflow, Kubeflow, or DVC and keen on adopting new ML engineering best practices.
Interested in large-scale data processing, distributed systems, LLMOps, and open to continuous learning and evolving ML infrastructure capabilities.