Role Descriptions: DevOps Engineer AI/MLMust Have:Proven experience building and deploying ML models in production environments| not just experimentation Strong background in time-series analysis and anomaly detection techniques (statistical and/or ML-based) Experience working with telemetry data: metrics| logs| traces| or events Proficiency in Python and relevant ML libraries (scikit-learn| PyTorch| TensorFlow| or similar) Familiarity with data pipeline design and feature engineering from semi structured/unstructured data Experience with at least one cloud platform for model deployment and pipeline orchestrationResponsibility of / Expectations from the Role Anomaly Detection Design and deploy ML models to detect anomalies across infrastructure metrics| application logs| traces| and database telemetry Develop adaptive baselines that account for seasonality| workload patterns| and service behaviour Reduce alert noise through intelligent thresholding and signal filtering 2. Data Correlation & Relationship Mapping Build models and pipelines that correlate signals across different telemetry sources to surface root cause insights Identify dependency relationships between services| infrastructure layers| and data flows Support incident response by surfacing correlated signals in near real-time 3. AIOps Innovation Identify and prototype additional high-value AIOps use cases: predictive monitoring| intelligent alerting| capacity forecasting| SLO breach prediction Contribute to the teams evolving roadmap for AI-augmented observability Work closely with the team to integrate models into existing Grafana dashboards and alerting workflows 4. Model Operations Own the full ML lifecycle: data preparation| model training| evaluation| deployment| and monitoring Ensure models are explainable| maintainable| and production-grade Document approaches| model decisions| and performance benchmarks
Essential Skills: DevOps Engineer AI/MLMust Have:Proven experience building and deploying ML models in production environments| not just experimentation Strong background in time-series analysis and anomaly detection techniques (statistical and/or ML-based) Experience working with telemetry data: metrics| logs| traces| or events Proficiency in Python and relevant ML libraries (scikit-learn| PyTorch| TensorFlow| or similar) Familiarity with data pipeline design and feature engineering from semi structured/unstructured data Experience with at least one cloud platform for model deployment and pipeline orchestrationResponsibility of / Expectations from the Role Anomaly Detection Design and deploy ML models to detect anomalies across infrastructure metrics| application logs| traces| and database telemetry Develop adaptive baselines that account for seasonality| workload patterns| and service behaviour Reduce alert noise through intelligent thresholding and signal filtering 2. Data Correlation & Relationship Mapping Build models and pipelines that correlate signals across different telemetry sources to surface root cause insights Identify dependency relationships between services| infrastructure layers| and data flows Support incident response by surfacing correlated signals in near real-time 3. AIOps Innovation Identify and prototype additional high-value AIOps use cases: predictive monitoring| intelligent alerting| capacity forecasting| SLO breach prediction Contribute to the teams evolving roadmap for AI-augmented observability Work closely with the team to integrate models into existing Grafana dashboards and alerting workflows 4. Model Operations Own the full ML lifecycle: data preparation| model training| evaluation| deployment| and monitoring Ensure models are explainable| maintainable| and production-grade Document approaches| model decisions| and performance benchmarks
Desirable Skills:
Keyword:
Skills: Digital : Artificial Intelligence(AI)~Digital : DevOps
Experience Required: 8-10
Pay: ₹100,000.00 - ₹160,000.00 per month
Work Location: In person
