---
title: "Satyadeep Singh - DevOps Engineer - Associate L2 in Noida | Tal"
description: "Meet Satyadeep Singh, DevOps & Site Reliability Engineer (L2) @ Publicis Sapient | AWS | Kubernetes | Terraform | Ansible | Helm | Docker | Dynatrace | Grafana | Prometheus | Akamai | RBAC | GitOps | ServiceNow based in Noida."
image: "https://tal.club/meet/satyadeep-singh/share-image"
---

# Satyadeep Singh on Tal

## Headline

DevOps & Site Reliability Engineer \(L2\) @ Publicis Sapient \| AWS \| Kubernetes \| Terraform \| Ansible \| Helm \| Docker \| Dynatrace \| Grafana \| Prometheus \| Akamai \| RBAC \| GitOps \| ServiceNow

## About

DevOps & Site Reliability Engineer \| Cloud FinOps \| Infrastructure Optimization I specialize in building resilient, cost\-efficient infrastructure that scales\. My core philosophy is that it’s not enough for a system to be "up"—it must be optimized for both performance and profitability\. Currently, at Publicis Sapient, I focus on high\-stakes SRE for global brands like Marriott, L'Oréal, and Loblaws\. A career highlight includes leading a Cloud FinOps initiative that slashed AWS expenditure by 68% \($100k\+ monthly savings\) through precise resource rightsizing and EKS optimization across Dev, QA, and Prod environments\. What I Do: • Infrastructure & Orchestration: Designing and maintaining high\-availability EKS and Kubernetes\-based workloads using Terraform and Ansible\. • Reliability & Observability: Engineering 99\.9% uptime solutions and reducing MTTR through deep\-dive forensic analysis using Dynatrace, Splunk, and the ELK Stack\. • Traffic & Security: Orchestrating zero\-downtime deployments and securing application delivery via Akamai CDN and traffic steering\. • Automation: Building robust CI/CD pipelines \(Jenkins\) and streamlining container strategies with strict Docker versioning\. • Beyond the terminal, I help students and early\-career professionals master Cloud workflows, System Design, and DevOps best practices\. I thrive on bridging the gap between complex backend architecture and seamless user experiences\. My goal is simple: ensure global platforms remain secure, low\-latency, and cost\-effective while building the next generation of engineers who can do the same\. Core Tech Stack: Cloud: AWS \(EKS, EC2, S3, RDS\), Azure Tools: Kubernetes, Docker, Terraform, Ansible, Akamai, Jenkins Monitoring: Dynatrace, Prometheus, Grafana, Splunk, PagerDuty Languages: Python, Shell/Bash

## Location

Noida

## Work experience

DevOps Engineer \- Associate L2 at Publicis Sapient, Apr 2025 \- Present · 1 yr 1 mo, Noida, • Reduced monthly AWS expenditure by 68% \($145k down to $45\.6k\) by leading a Cloud FinOps initiative that optimized infrastructure resources across Dev, QA, Pre\-Prod, and Prod environments\. • Achieved zero\-downtime during deployment windows by orchestrating traffic steering via Akamai to standby servers, ensuring seamless updates and uninterrupted service for the booking platform\. • Enhanced environment stability and rollback reliability by implementing a strict Docker image\-tagging and versioning strategy, streamlining containerized deployments for the HVMI ecosystem\. • Reduced transaction failure rates by conducting deep\-dive log analysis on booking and cancellation telemetry, identifying technical root causes and providing actionable feedback to the product team\. • AWS \(EC2, S3, RDS\), Docker\(Versioning/Tagging\), Kubernetes \(EKS\), Akamai CDN, Terraform, Jenkins, Python, Patching activity, Certificates, Cloud FinOps \(Cost Optimization\), Zero\-Downtime Deployments, Log Telemetry\(Traffic Steering\)\.

DevOps Engineer \- Associate L1 at Publicis Sapient, Nov 2022 \- Apr 2025 · 2 yrs 6 mos, Gurugram, • Ensured 99\.9% site availability for luxury brands \(YSL, Aesop, Lancôme, Kiehl's, Kérastase\) by implementing auto\-scaling policies and traffic shedding strategies to handle massive surges during peak sale events\. • Minimized revenue loss from failed transactions by engineering real\-time observability dashboards to detect and resolve order placement bottlenecks and checkout flow interruptions\. • Optimized visual consistency and user experience by automating CDN cache invalidation and integrity checks for product assets, ensuring high\-resolution order images remained available globally\. • Reduced Mean Time to Recovery \(MTTR\) during site outages by standardizing a P1 incident response playbook for the APAC region, facilitating rapid cross\-functional coordination during critical sale windows\. • AWS \(EC2, Auto Scaling\), Akamai CDN, Dynatrace, Splunk, Kubernetes, Docker, Jenkins, Site Availability \(99\.9%\), Peak Sale Scaling, Real\-time Observability\.

DevOps Engineer \- Associate at Publicis Sapient, May 2022 \- Nov 2022 · 7 mos, Gurugram, • Maximized high\-availability for national retail operations by managing the end\-to\-end incident lifecycle for the mission\-critical grocery billing ecosystem, maintaining 99\.9% uptime for POS and e\-commerce transaction pipelines\. • Reduced Mean Time to Resolution \(MTTR\) for P1/P2 outages by orchestrating real\-time technical response via ServiceNow, rapidly diagnosing root causes in the order placement flow to prevent revenue loss during peak retail windows\. • Enhanced production stability and observability by performing deep\-dive forensic analysis on billing telemetry and error logs, identifying recurring systemic bottlenecks and providing architectural feedback to improve application resilience\. • Streamlined deployment reliability for distributed retail nodes by collaborating with cross\-functional teams to validate patches and configuration changes, ensuring seamless updates to POS systems across nationwide store environments\. • Splunk, ELK Stack, ServiceNow, Jira, Confluence, Linux/Unix, Bash Scripting, MTTR Optimization, High\-Availability POS Systems, Incident Lifecycle Management, LaunchDarkly, PagerDuty

Software Engineer at Jio, Jan 2022 \- Apr 2022 · 4 mos, Navi Mumbai, • Optimized HR decision\-making processes by developing a Python\-based text analytics engine to categorize raw employee feedback and support tickets\. • Streamlined data\-driven predictions by implementing machine learning models to identify recurring workplace issues, enhancing department response accuracy\. • Improved data reliability for a core HR product by engineering automated cleaning and preprocessing pipelines for large\-scale unstructured text datasets\. • Python, Text Analytics, Machine Learning, Data Science, Pandas, NumPy, Scikit\-learn\.

Machine Learning Intern at iNeuron\.ai, Nov 2020 \- Feb 2021 · 4 mos, Bengaluru, Karnataka, India

Machine Learning Intern at Technocolabs, Sep 2020 \- Oct 2020 · 2 mos, Indore, Madhya Pradesh, India

## Education

Bachelor of Technology \- BTech, Computer Science, Maharaja Ranjit Singh Punjab Technical University \(MRSPTU\), Bathinda

[View the original profile](https://tal.club/meet/satyadeep-singh)

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