1 - 6 years

12 - 13 Lacs

Posted:-1 days ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

AI/ML Engineer :

THE ROLE:

We are looking for an

AIOps Software Development Engineer

who designs and builds intelligent systems that automate IT operations using AI/ML, big data analytics, and automation tools. The role focuses on predicting incidents, reducing downtime, automating root-cause analysis, and improving overall system reliability.

KEY RESPONSIBILITIES:

1. AI/ML Engineering

Build and deploy ML models for anomaly detection, event correlation, log analysis, capacity forecasting, and predictive maintenance.
Develop real-time data pipelines for metrics, logs, traces, and alerts.
Perform feature engineering on operational data (system metrics, logs, traces, events).

2. Software Development & Automation

Design and develop automation workflows for self-healing and preventive remediation.
Build microservices, APIs, and automation platforms to integrate with monitoring tools.
Implement end-to-end CI/CD pipelines.

3. Monitoring & Observability

Integrate with tools like Nagios, Prometheus, PowerBI, Grafana, ELK/EFK, Splunk, AppDynamics, OpenTelemetry, etc.
Develop dashboards, alert systems, and visualization for operational insights.
Use distributed tracing and log aggregation to support automated analysis.

4. Incident & RCA Prediction & Fix Automation

Build ML-based correlation engines for RCA.
Develop systems to predict incidents based on patterns in logs/metrics.
Automate incident detection, ticket classification, and probable cause inference.

5. Reliability Engineering

Work with SRE teams to implement automated remediation (restart services, scale resources, patch nodes, heal containers, etc.).
Improve SLAs, SLOs, and MTTR using automation and ML insights.

PREFERRED EXPERIENCE:

Programming & Development

Python (must), Java/Go (optional) Strong understanding of data structures & algorithms API development (REST, gRPC) Microservices & containerization (Docker, Kubernetes)

AI/ML Skills

Machine learning (supervised, unsupervised) Anomaly detection algorithms Model deployment (MLflow, SageMaker, custom APIs)

Data Engineering

Kafka, Spark, Flink, Kinesis, or similar streaming systems SQL/NoSQL databases

DevOps & Cloud

CI/CD tools: Jenkins, GitHub Actions, GitLab CI Cloud platforms: AWS, Azure, GCP IaC: Terraform, Ansible

ACADEMIC CREDENTIALS:

  • Bachelor s degree in Computer/Software Engineering, Computer Science, or related technical discipline

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Advanced Micro Devices, Inc

Semiconductors

Sunnyvale

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