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Specialized MLOps/time-series role, metro location, and mid-seniority yield moderate competition.
Role demands specialized ML and MLOps experience, limiting cross-domain transferability.
Explicit 6–12 years plus detailed mandatory MLOps/ML and infra tech stack increases shortlisting strictness.
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Own end-to-end machine learning platform development for network analytics anomaly detection, including training pipeline, orchestration service on Kubernetes, and web-based control panel.
Develop and maintain ML training pipelines (LSTM and KMeans models), orchestration APIs, UI via Streamlit, Helm deployments, and Docker packaging.
Collaborate on ML lifecycle management, including model versioning with MLflow, deployment automation, and monitoring drift metrics.
6 to 12 years of experience in machine learning engineering and MLOps platform development.
Proficiency in Python 3.11, including async programming and FastAPI/Pydantic frameworks.
Experience with LSTM or recurrent neural networks for time-series anomaly detection (PyTorch), clustering algorithms like KMeans.
Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or equivalent practical experience.
Experienced in integrating ML model training with Kubernetes orchestration and Helm/Ansible for deployment automation.
Skilled in building user interfaces for non-technical stakeholders to manage ML lifecycle using Streamlit or similar tools.
Strong focus on maintaining production-grade ML workflows including version control, drift detection, and scalable pipeline architecture.