Description

Job Responsibilities include:

Design, scale, and maintain highly available infrastructure for LLM training, fine-tuning, and inference workloads.

Architect and implement agentic AI systems for automated alert triage, root cause analysis, and self-healing.

Optimize GPU utilization, cluster health, and orchestration across Kubernetes-based environments.

Define and monitor AI-specific SLOs/SLIs such as latency, throughput, and cost efficiency.

Ensure reliability of vector databases and RAG pipelines for AI data processing.

Integrate AI-driven incident management with ChatOps and enforce security guardrails for GenAI systems.

Required Qualifications:

Bachelor’s or master’s degree in computer science, Engineering, Data Science, or a related field.

8–10 years of experience in Site Reliability Engineering, DevOps, or AI/ML infrastructure roles.

Strong expertise in Kubernetes, cloud platforms (AWS/GCP/Azure), and infrastructure automation (Terraform, CI/CD).

Proven experience in GenAI/LLM systems, including orchestration frameworks (LangChain, AutoGen), vector databases, and building reliable, scalable AI platforms

Key Skills

Education

Any Graduate