Job Summary

The Splunk AI Platform and Services team builds and operates the core runtime and developer infrastructure powering AI across Splunk and Cisco. We run multi-tenant, large-scale LLM inference, VectorDB/RAG, Model Context Protocol (MCP), and AI DevX services across hybrid multi-cloud environments.

Sitting at the intersection of Site Reliability Engineering, Distributed Systems, and ML Infrastructure, our mission is to ensure these mission-critical AI workloads remain highly available, scalable, secure, and cost-productive in production.

Responsibilities

- Scale Core AI Services: Operate and optimize multi-tenant LLM inference, model serving, VectorDB/RAG, and MCP platforms across global cloud and GPU clusters.
- Drive Platform Resiliency: Establish and monitor SLOs, SLIs, automated health checks, and observability dashboards (Prometheus, Grafana, Splunk, OpenTelemetry).
- Automate Operations Toil Reduction: Build automation for seamless rollouts, autoscaling, canary deployments, self-healing, and disaster recovery.
- Optimize GPU Inference Performance: Tune throughput, latency, model scheduling, batching, caching, and resource allocation using Kubernetes, vLLM, and Ray.
- Incident Command Prevention: Lead complex production incident response, conduct 5-Why root-cause analyses, and implement systemic remediations to eliminate repeat outages.
- Cross-Functional Enablement: Partner with AI researchers, product developers, and platform teams on production readiness reviews (PRRs) and participate in a sustainable on-call rotation.

Minimum Qualifications





- Education Experience: Bachelor s degree in CS/Engineering with 5+ years of relevant experience, or Master s with 3+ years.
- Software Development: 3+ years of programming experience in Python, Go, or Java building automation, tooling, or production backend services.
- Cloud-Native Operations: Solid experience running distributed, containerized systems in production using Kubernetes, microservices, and CI/CD.
- SRE Observability: Proven background in production operations, distributed troubleshooting, and monitoring stacks (Prometheus, Grafana, Splunk, or OpenTelemetry).
- Cloud Platforms: Hands-on working knowledge of AWS, Azure, or GCP.

Preferred Qualifications

- LLM Inference Frameworks: Hands-on experience with vLLM, TensorRT-LLM, Ray Serve, or KServe.
- GPU Orchestration: Experience managing GPU scheduling, capacity planning, model parallelism, and performance profiling.
- Stateful Retrieval Data Stores: Experience operating Vector databases, search engines, or distributed cache layers (e.g., Milvus, Pinecone, Redis).
- GenAI Architectures: Familiarity with RAG pipelines, model routing, Model Context Protocol (MCP), and agent runtimes.
- Advanced SRE Design: Track record designing multi-region high-availability systems, automated remediation, and chaos engineering practices.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Machine Learning Engineer (Hyderabad)
🏢 Cisco
📍 Hyderabad