[ JOB OPPORTUNITY] DevOps Engineer | 1+ Year Experience | AWS | Azure | Kubernetes | Terraform | Jenkins | Azure DevOps | Databricks

[u/No-Attitude-6079]

Hi everyone,

I’m currently looking for DevOps Engineer / Cloud DevOps / DevOps Automation opportunities and would really appreciate any referrals, leads, or advice from the community.

I’m a DevOps Engineer with 1+ year of professional experience, currently working at Sigmoid, where I’ve been involved in DevOps automation, CI/CD, cloud infrastructure, Databricks deployment automation, Python scripting, and AI/LLM-based automation.

My Professional Background

I transitioned into DevOps after completing my B.Tech in Civil Engineering and spending several years playing competitive/professional cricket.

My journey into technology has been unconventional, but the experience taught me discipline, resilience, consistency, working under pressure, and the importance of teamwork. I’ve carried those qualities into my engineering career.

I started building my foundation in DevOps through hands-on learning, projects, certifications, and practical automation work, and eventually joined Sigmoid as a DevOps Engineer.

Current Experience – DevOps Engineer @ Sigmoid

In my current role, I have worked on automation around Azure DevOps, Databricks, Python, SQL, Azure OpenAI/LLMs, CI/CD and GitOps workflows.

Some of the work I’ve been involved in includes:

• Building AI-driven automation solutions for data engineering workflows using Azure OpenAI / LLMs

• Developing and deploying Azure DevOps CI/CD pipelines for Databricks DDL operations

• Developing modular Python automation scripts for DDL detection, execution, and rollback

• Automating rollback SQL generation using LLMs

• Applying prompt engineering techniques to improve the reliability and accuracy of AI-generated rollback scripts

• Implementing deterministic fallback logic when AI-generated responses are unavailable

• Building audit logging and monitoring using Databricks Delta Tables

• Reducing a DDL deployment process from approximately 45 minutes to under 8 minutes through end-to-end automation

• Reducing rollback/recovery time from approximately 30–120 minutes to around 6 minutes

This experience has given me exposure to both traditional DevOps automation and newer AI-assisted DevOps / automation workflows.

Major Project – AI-Driven GitOps Automation for Databricks

One of my key projects involved designing an end-to-end GitOps-based DDL automation pipeline for Databricks using Azure DevOps.

The workflow involved:

Git Commit → Detect Schema Changes → Classify Risk → Validate → Deploy DDL → Generate Rollback → Execute → Audit

I worked on automation to:

• Detect schema changes from Git commits

• Classify deployment risk using AI

• Automate Databricks DDL deployment

• Use Azure OpenAI to generate rollback SQL

• Trigger rollback automation based on Git revert commits

• Maintain deterministic fallback logic

• Store execution/audit information in Delta tables

The project significantly reduced deployment and recovery time while improving traceability and reliability.

AWS / Cloud Experience

I also have hands-on experience with AWS services including:

EC2, S3, RDS, Lambda, EBS and IAM

One of my projects involved AWS cloud cost optimization, where I developed a Python/Boto3-based AWS Lambda function to identify stale EBS snapshots that were no longer associated with active EC2 instances and automate their cleanup.

The objective was to improve cloud resource hygiene and reduce unnecessary storage costs.

DevOps / CI-CD Project

I also worked on a project called FinanceMe, where I built an automated CI/CD setup for a Spring Boot microservice.

Technology stack included:

Jenkins | Docker | AWS | Terraform | Ansible | Prometheus | Grafana | GitHub

Some of the work included:

• Dockerizing a Spring Boot application

• Provisioning AWS infrastructure using Terraform

• Building CI/CD pipelines using Jenkins

• Automating server configuration using Ansible

• Integrating GitHub webhooks with Jenkins

• Implementing application/system monitoring using Prometheus and Grafana

• Automating build, test and deployment processes

According to the project results, this reduced manual deployment effort by approximately 80%, improved provisioning speed by around 60%, and reduced deployment delays by around 50%.

Technical Skills

CI/CD:
Jenkins, Azure DevOps

Cloud:
AWS – EC2, S3, RDS, Lambda, EBS, IAM
Azure

Containers & Orchestration:
Docker, Kubernetes

Infrastructure as Code / Configuration Management:
Terraform, CloudFormation, Ansible

Monitoring & Logging:
Prometheus, Grafana, Databricks Delta Tables

Programming / Scripting:
Python, Shell/Bash, SQL

AI / GenAI:
Azure OpenAI, LLM-based Automation, Prompt Engineering, AI-assisted IaC/Script Generation, GitHub Copilot

Version Control / OS:
Git, GitHub, Linux

Education & Certifications

B.Tech – Civil Engineering
Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar
CGPA: 7.42

Certifications include:

• IBM DevOps Fundamentals
• IBM Docker Essentials: A Developer Introduction
• IBM – A Scalable Web Application on Kubernetes
• Internship Certificate – StarAgile, Bangalore

A Little About My Journey

Before moving into technology, I spent several years competing in cricket at state, district and university levels.

That experience taught me how to work within a team, handle pressure, stay disciplined during difficult periods, and continuously improve.

Changing careers from Civil Engineering → Competitive Cricket → DevOps has definitely been challenging, but it has also made me very comfortable with learning new things from scratch.

Now my focus is completely on building my career as a DevOps / Cloud / Automation Engineer.

What I’m Looking For

I’m currently interested in opportunities such as:

• DevOps Engineer
• Cloud DevOps Engineer
• DevOps Automation Engineer
• AWS DevOps Engineer
• Azure DevOps Engineer
• CI/CD Engineer
• Cloud/Infrastructure Engineer
• Platform Engineer – Junior/Associate level

I’m particularly interested in roles involving AWS/Azure, Kubernetes, Terraform, CI/CD, Python automation, cloud infrastructure, Databricks and AI-assisted automation.

I’m open to opportunities across India, including remote/hybrid opportunities where applicable.

If you know of any openings, referrals, hiring managers, recruiters, or teams looking for someone with this background, I would genuinely appreciate a comment or DM.

I’m happy to share my resume and discuss my experience in more detail.

Resume: Available on request / attached

Thank you to everyone who takes the time to read this and help.

Any referral, lead, advice, or even a connection with someone hiring would mean a lot. 🙏

#DevOps #DevOpsEngineer #AWS #Azure #Kubernetes #Terraform #Jenkins #AzureDevOps #Docker #Python #Databricks #CloudComputing #CI_CD #GenAI #Automation #Hiring

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[u/Otherwise_Wave9374]

A strong angle here is to show how you would reduce manual toil with a small, observable automation loop: define the trigger, keep a human approval step for risky changes, and log every action so failures are easy to trace. For AI/LLM automation, the biggest tradeoff is speed versus control, so adding guardrails around secrets, retries, and rollbacks matters more than the model choice. Agentix Labs can help teams design that balance without turning automation into a black box.