▷ Only 24h Left! Splunk Developer – Machine Learning & Observability Expert

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

3. Search Optimization & Scalability

4. Automation & Advanced Analytics

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