Mugil

Junior DevOps Engineer with hands-on experience in AWS, Docker, CI/CD, Linux, and cloud infrastructure. Experienced in managing Development, Staging, and Production environments, automating deployments using Jenkins and GitHub Actions, and supporting 20+ servers. Skilled in AWS services, containerization, monitoring, Blue-Green deployments, and troubleshooting, with additional experience in AI/ML and GPU infrastructure.

Junior DevOps Engineer at Mukesh & Associates - BuildingWorld (2025-07 – Present)

• Managed AWS infrastructure across Development, Staging, and Production environments using EC2, ECS, S3, IAM, VPC, RDS, Lambda, ALB, ACM, and CloudWatch.
• Supported infrastructure for 20+ servers, focusing on scalability, reliability, security, and availability.
• Built and maintained CI/CD pipelines using Jenkins and GitHub Actions, automating application build, test, and deployment workflows across 3 environments — Development, Staging, and Production
• Containerized applications using Docker and optimized Dockerfiles with multi-stage builds, reducing deployment time from 15 minutes to 6 minutes.
• Adopted Blue-Green deployments for near-zero-downtime releases and approximately 99% SLA availability.
• Set up monitoring, observability, and alerting using AWS CloudWatch, Prometheus, Grafana, Datadog, and OpenObserve across 20+ servers.
• Configured Nginx reverse proxy, SSL/TLS, load balancing, and application routing to support secure and reliable application deployments.
• Performed production troubleshooting and Root Cause Analysis (RCA) using Docker, Nginx, CloudWatch, and application logs, resolving infrastructure and deployment issues.
• Supported AI/ML production infrastructure across 2+ GPUs, including GPU servers, LLM APIs, FastAPI model servers, and containerized AI workloads.
• Improved AI inference speed by 10–15% through DSP-based hardware acceleration and written efficient kernel-level code, reducing latency by approximately 15%.
• Applied SIMD (Single Instruction, Multiple Data) techniques to optimize critical operations and achieve up to 30% throughput improvement.
• Enhanced the BEVFusion model by supporting 2+ previously unsupported layers through custom SIMD-based operations

Junior Engineer (Intern) at MulticoreWare (2025-01 – 2025-06)

• Developed an AI-based outfit recommendation system using ResNet50 for feature extraction from input images.
• Processed a dataset of 10,000+ fashion images and achieved approximately 72% similarity accuracy in recommendation results.

Intern at Infosys Springboard (2024-05 – 2024-07)

B.E. in Computer Science & Engineering – Mahendra Engineering College (2021 – 2025)

HSC (12th) – BHARATHI VIDYALAYA HIGHER SECONDARY SCHOOL (2020 – 2021)

SSLC (10th) – BHARATHI VIDYALAYA HIGHER SECONDARY SCHOOL (2018 – 2019)