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MLOps Engineer AIOps & Observability

MLOps Engineer AIOps & Observability

IBU Consulting

3 - 7 years

4 - 7 Lacs

hubli mangaluru mysuru bengaluru belgaum

Posted:1 month ago| Platform:

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Skills Required

root cause analysis automation data science machine learning data processing incident management apache forecasting analytics python

Work Mode

Work from Office

Job Type

Full Time

Job Description

Job Description

Job Summary

We are looking for an experienced MLOps Engineer or Data Science Engineer with strong expertise in machine learning operations and observability platforms. The ideal candidate will build intelligent AIOps capabilities that enhance proactive monitoring, automated insights, and predictive incident management across cloud-scale environments.

Key Responsibilities

1. AIOps Model Development

Design, develop, and deploy machine learning models tailored for observability use cases such as anomaly detection, predictive alerting, incident forecasting, and automated root cause analysis.
Leverage time-series data from distributed infrastructure, application telemetry, and cloud environments.

2. Time-Series Analytics & Forecasting

Build and optimize ML models using Python libraries such as scikit-learn, Prophet, TensorFlow, PyTorch, and statsmodels.
Identify behavioral patterns, detect performance degradations, and predict system failures to support proactive incident prevention across Azure ecosystems.

3. Data Pipeline Engineering

Architect scalable, real-time data pipelines using Apache Kafka for ingesting high-volume observability data.
Implement stream processing using Apache Flink to transform, enrich, and deliver high-quality telemetry to ML models.

4. Azure Databricks Integration

Utilize Azure Databricks for large-scale data processing, feature engineering, model training, and analytics workflows.
Develop automated MLOps pipelines supporting continuous training and model evolution based on changing infrastructure patterns.

5. Observability Platform Expertise

Apply hands-on experience with platforms such as Datadog or similar observability tools.
Understand the structure of metrics, logs, and traces to translate observability data into meaningful ML features and intelligent automation capabilities.

6. MLOps Lifecycle Management

Establish complete MLOps workflows including model versioning, automated testing, CI/CD pipelines, performance monitoring, and feedback loops.
Ensure deployed AIOps models deliver reliable, accurate, and actionable insights that reduce MTTD (Mean Time to Detection) and MTTR (Mean Time to Resolution).

Required Skills & Qualifications

Proven experience in MLOps, Data Engineering, or Data Science.
Strong knowledge of machine learning for time-series data and AIOps use cases.
Hands-on experience with Kafka, Flink, and Azure Databricks.
Proficiency in Python and ML libraries (TensorFlow, PyTorch, scikit-learn, etc.).
Understanding of observability ecosystems (e.g., Datadog, Prometheus, Elastic, New Relic).
Experience deploying ML models in production and managing full model lifecycle.

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MLOps Engineer AIOps & Observability