Splunk Developer – Machine Learning
3 days ago
Position Overview
We are seeking an experienced Splunk Developer to join Enterprise Monitoring team. The ideal candidate will have 6-8 years of hands-on experience with Splunk, including search optimization, machine learning capabilities, and deep technical expertise in log analysis and monitoring solutions.
Key Responsibilities
1. Machine Learning & AIOps
- Design and implement ML-based monitoring solutions using Splunk's ML Toolkit (MLTK).
- Build predictive and anomaly detection models for infrastructure metrics (CPU, memory, latency, etc.).
- Develop custom ML use cases—such as log clustering, failure prediction, and capacity forecasting.
- Optimize real-time analytics for large-scale datasets (millions of events/sec).
2. Splunk Observability & Unified Dashboards
- Implement end-to-end observability using Splunk Observability Cloud (APM, RUM, Log Observer, Infrastructure Monitoring).
- Design unified dashboards that consolidate metrics, traces, and logs across hybrid cloud (AWS/Azure/GCP) and on-prem systems.
- Correlate ML insights with observability data to automate root cause analysis (RCA).
- Integrate with OpenTelemetry, Prometheus, and distributed tracing for full-stack visibility.
3. Search Optimization & Scalability
- Reduce search overhead by optimizing SPL queries and data model acceleration.
- Implement summary indexing and data sampling for high-volume environments.
4. Automation & Advanced Analytics
- Python scripting for custom ML pipelines, API integrations, and automation.
- Leverage Splunk's REST API for dynamic dashboarding and alerting.
- Splunk App Development & Integrations: Build custom apps and integrate Splunk with third-party tools (ITSM, CI/CD, Cloud platforms).
Mandatory Skills
6-8 years of Splunk development with proven ML use cases (anomaly detection, forecasting, clustering).
Hands-on Splunk Observability Cloud (SignalFX/APM/IM/Log Observer).
Experience building unified dashboards for infrastructure (servers, Kubernetes, cloud, network).
Strong Python for ML (Pandas, Scikit-learn, TensorFlow/PyTorch is a plus).
Search optimization at scale (data models, accelerated reports, summary indexing).
Familiarity with DevOps practices (CI/CD pipelines, Terraform, Ansible).
Experience with OpenTelemetry, Prometheus, and distributed tracing.
Knowledge of IT operations, SRE (Site Reliability Engineering), and incident management.
Splunk certifications (MLTK, Observability, Core Certified Power User).
Knowledge of MLOps pipelines (model training, deployment, monitoring).
Experience with OpenTelemetry, Prometheus, and Grafana integrations.
Why This Role?
This is not just a Splunk admin role—we need someone who can:
Build AI-driven monitoring to predict failures before they happen.
Turn observability data into actionable insights with ML-powered dashboards.
Optimize performance across hybrid cloud, containers, and legacy systems.
If you've built ML models in Splunk and designed observability solutions for complex environments, this role is ideal for a Splunk expert who can optimize performance, enhance observability, and implement AI-driven monitoring solutions to improve system reliability and efficiency
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