SITE RELIABILITY ENGINEER — KUBERNETES, CLOUD & AI-ASSISTED AUTOMATION
Site Reliability Engineer with 3.5+ years across cloud operations and infrastructure engineering. I run containerised workloads on Kubernetes and Docker, with AWS as my primary platform alongside Azure, and I build the infrastructure as code in Terraform and Ansible rather than clicking through consoles. Day to day that means CI/CD pipelines, Prometheus and Grafana monitoring, on-call incident response, and root cause analysis. Alongside that I've built a multi-agent AI platform using LLMs, RAG, and LangChain/CrewAI for automated Kubernetes and cloud incident analysis. AWS Certified Solutions Architect – Associate.
Stack
Builds
A multi-agent AI platform that analyses cloud and Kubernetes incidents using LLMs and RAG, with specialised agents for log analysis, root-cause detection, and troubleshooting recommendations. Fine-tuned an open-source model with LoRA and QLoRA, comparing accuracy, memory use, and inference speed across runs. Retrieval built on vector search with LangChain and CrewAI agent workflows; containerised and deployed on Docker, Kubernetes, and AWS with CI/CD and monitoring.
View on GitHub →A full-stack hiring platform built end to end: Node.js/Express backend, SQL database, REST API, and a responsive web front end. The interesting part is the multi-model AI routing layer — Claude, Gemini, and DeepSeek APIs with automatic failover, applying reliability and redundancy patterns to a production AI service. I own the full deployment lifecycle, including environment config, release packaging, admin tooling, activity logging, and API cost monitoring.
Multi-tier environments built on AWS EC2 with Linux, containerised workloads on Docker, and CI/CD workflows orchestrated through Jenkins and GitHub Actions — a working sandbox for testing patterns before they reach production.
View on GitHub →Deployment log
Credentials
Contact
$whoami
mayank-agarwal — site reliability engineer, jaipur, open to bangalore (hybrid)
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