Role Overview
As a Senior DevSecOps Engineer, you will be the core driver behind the platform architecture, automation, CI/CD pipeline infrastructure, and security integration across the OpenForge 3.0 platform. In addition to managing environment lifecycle, release strategies, and AWS/Kubernetes deployments, you will play a pivotal role in AI enablement—integrating AI/ML workflows, AI-assisted DevOps tools, and AI governance into the platform ecosystem.
Key Responsibilities
CI/CD, Automation & Release Management
Architect, standardize, and maintain enterprise-grade CI/CD pipeline templates (GitLab CI, GitHub Actions, Jenkins, or Harness) across all OpenForge 3.0 modules.
Implement Zero-Downtime deployment strategies (Blue/Green, Canary, Rolling Updates) and automated release management workflows.
Automate Infrastructure as Code (IaC) using Terraform, CloudFormation, or Pulumi to enable repeatable, drift-managed environment provisioning.
AWS Cloud, Kubernetes & Platform Operations
Manage, optimize, and scale production-grade AWS cloud infrastructure (EKS, IAM, VPC, RDS, S3, CloudFront, GuardDuty).
Manage end-to-end Kubernetes cluster operations (Helm, ArgoCD/Flux for GitOps, Service Mesh like Istio/Linkerd, Ingress controllers).
Implement multi-environment strategies (Dev, Staging, UAT, Prod) with strict segregation, auto-scaling, and drift detection.
Shift-Left Security & DevSecOps
Integrate automated security tooling into pipelines:
SAST / DAST: SonarQube, Snyk, Veracode, OWASP ZAP
Container & Image Scanning: Trivy, Clair, AWS ECR scanning
SCA & License Compliance: Dependency-Track, Snyk
Secrets Management: HashiCorp Vault, AWS Secrets Manager
Enforce security policies, compliance standards (SOC 2, ISO 27001, HIPAA, or PCI-DSS), and automated vulnerability patching across Kubernetes and AWS.
AI Enablement for OpenForge 3.0
Drive the enablement and integration of AI/ML tools and LLM capabilities into the platform ecosystem (e.g., AI-driven incident management, AI-assisted code scanning, automated test generation, and predictive autoscaling).
Build and maintain infrastructure support for hosting/serving local or cloud-based AI models (Vector DBs, GPU node groups on EKS, Ollama, LangChain, or AWS Bedrock/SageMaker endpoints).
Establish guardrails for secure AI usage, including prompt/data privacy, model security scanning, and cost tracking for AI API/GPU usage.
Experience & Qualifications
Experience: 6+ years of experience in DevSecOps, Site Reliability Engineering (SRE), or Cloud Platform Engineering.
Kubernetes Mastery: Deep hands-on experience managing and securing production Kubernetes environments on AWS (EKS).
Security First: Demonstrated background in embedding security practices throughout the software development lifecycle (SDLC).
AI Expertise: Practical experience or strong aptitude in integrating AI tools, LLMs, or MLOps pipelines into cloud platforms.