Prajol Sethi

Software Engineer

Mumbai, Maharashtra, India6 yrs 1 mo experience

Most Likely To SwitchAI Enabled

Key Highlights

Architected a DevOps platform for 150+ developers.
Reduced engineering effort by 65% using AI tools.
Migrated 120+ payment APIs across multiple countries.

Stackforce AI infers this person is a DevOps Engineer specializing in Fintech with a focus on cloud infrastructure and automation.

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Skills

Core Skills

DevopsSite Reliability EngineeringKubernetesInfrastructure As Code (iac)Leadership

Other Skills

Large Scale DevelopmentModel Context Protocol (MCP)Chat-GPTClaude codeGitHub Copilotn8nCI/CDShell ScriptingGitRelease EngineeringGoogle Kubernetes Engine (GKE)NexusSplunkContinuous Integration and Continuous Delivery (CI/CD)Sonarqube

About

🏆 GCP ACE Certified | 3x HSBC Circle of Excellence winner | Currently exploring AI-native DevOps — MCP servers, N8N automation, and developer self-service platforms. When I joined HSBC as a Trainee in 2022, I was building CI/CD pipelines for one team. Three years later, I'd architected a DevOps platform used by 150+ developers across 12 service lines — and led the migration of 120+ payment APIs across the US, UK, Hong Kong, and China onto Kubernetes. 🌏 That scale taught me what good DevOps actually looks like in a regulated environment: not just fast deployments, but compliant, observable, and recoverable ones. I work across the full stack — Kubernetes (GKE, EKS), Terraform, Jenkins, Helm, Istio, Datadog, Prometheus — with a particular focus on platform reliability, SLO/SLI design, and infrastructure automation that genuinely reduces toil. ⚙️ Some of what I've built: 🔹 Reusable Terraform modules that cut new-project setup from days to hours 🔹 Auto-remediation scripts that reduced manual GCP ops by 80% 🔹 Blue-green and canary deployment workflows with automated rollback 🔹 An AI-assisted tooling layer using GitHub Copilot and Claude that cut per-deliverable engineering effort by 65% 📩 Open to Senior DevOps Engineer, SRE, and Platform Engineering roles in high-scale or regulated environments.

Experience

6 yrs 1 mo

Total Experience

2 yrs

Average Tenure

3 yrs 8 mos

Current Experience

Hsbc

3 roles

Senior Software Engineer

Promoted

Mar 2026 – Present · 2 mos

1. Spearheading AI-assisted DevOps development by integrating ChatGPT, Claude, and GitHub Copilot into daily workflows reducing per-deliverable effort by 65% , including full end-to-end testing.
2. Standardized CI/CD deployment patterns for backend, frontend, Spring Boot, and Python stacks across 12 service lines — enforcing consistency and reducing per-team pipeline configuration effort.
3. Prototyping MCP server and N8N workflow integrations to enable developer self-service, reducing dependency on platform team for routine deployment and infra requests.

Large Scale DevelopmentModel Context Protocol (MCP)Chat-GPTSite Reliability EngineeringClaude codeGitHub Copilot+2

Devops Engineer

Jun 2023 – Feb 2026 · 2 yrs 8 mos

1. Led Java 17 + Spring Boot migration and deployed 120+ APIs across UK, HK, US, and China onto HSBC’s Kubernetes platform (IKP) with Kong Gateway, improving release speed, compliance, and API reliability.
2. Developed several automated utilities scripts to auto-remediate GCP policy violations, reducing manual workload by almost 80%.
3. Containerized and deployed APIs on Kubernetes with Kong Gateway, integrating tools like Git, Sonar, NexusIQ, Helm, Vault, and AppDynamics to streamline deployments and improve code quality to 90%+.
4. Built Terraform-based CI pipelines and a Source of Truth (SOT) framework for GCP projects, automating IAM role provisioning, policy enforcement, and project configuration.
5. Supported multiple domains on Google Cloud Platform (GCP) with API deployments, Cloud SQL and MongoDB activities.
6. Developed and maintained Datadog dashboards and monitoring solutions, improving visibility and reducing incident response time by 20%.

Shell ScriptingGitKubernetesRelease EngineeringDevOpsGoogle Kubernetes Engine (GKE)+27

Trainee Software Engineer

Aug 2022 – Jun 2023 · 10 mos

1.Automated CI/CD pipelines for IDL project → accelerated releases and improved delivery speed by 30%.
2.Migrated applications from On-Prem → GKE (GCP), ensuring secure, scalable deployments.
3.Built a cost-optimization pipeline for dev/test environments → cut cloud spend by 40%.
4.Strengthened security posture by fixing vulnerabilities and achieving MODE2 compliance.
5.Delivered multiple API releases in cloud-native environments, improving team release velocity.

KubernetesSoftware Development Life Cycle (SDLC)Azure DevOps ServicesPython (Programming Language)JenkinsDevOps