Splunk Developer – Machine Learning

6 days ago

Hyderabad, Telangana, India ADP Full time

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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