Senior DevSecOps Engineer

Job Title: DevSecOps Senior Engineer

Location: Torrance, CA

Work Model: Onsite

Work Type: Onsite 4 days/week

Interview Mode: Face-to-Face

Key Requirement: Candidates must be authorized to work in the U.S. for any employer on a W-2 basis without requiring current or future visa sponsorship. Qualified candidates who meet these requirements are encouraged to apply.

WHAT’S NEEDED

Minimum Educational Qualifications - BS/MS degree

• 5-10 years of experience in DevOps, DevSecOps, Platform Engineering, Cloud Engineering, or related disciplines supporting enterprise-scale applications and distributed systems.
• 3+ years of experience in a senior technical role such as DevSecOps Lead, Platform Engineer, Senior DevOps Engineer, or Site Reliability Engineer (SRE).
• Strong hands-on experience with AWS cloud services including EKS/ECS, Lambda, IAM, VPC, CloudWatch, S3, RDS, Kinesis, MQ, RedShift, Secrets Manager, SNS, SQS, EventBridge, ElastiCache etc.
• Design, build, and maintain CI/CD pipelines using Jenkins, GitLab CI/CD, GitHub Actions, AWS CodePipeline, ArgoCD, or similar technologies.
• Implement Infrastructure as Code (IaC) using Terraform, CloudFormation, Helm, and related automation frameworks.
• Strong experience with Docker, Kubernetes, Amazon EKS, and container platform operations.
• Implement DevSecOps practices including vulnerability management, secrets management, policy enforcement, compliance validation, and software supply chain security.
• Strong knowledge of cloud security including IAM, RBAC, encryption, OAuth2/OIDC, JWT, network security, and Zero Trust principles.
• Build and manage observability, monitoring, logging, and alerting solutions using CloudWatch, Prometheus, Grafana, ELK/OpenSearch, Datadog, Splunk, or similar tools.
• Programming experience in Python, Bash, Go, or similar scripting languages.
• Drive platform reliability, resiliency, operational excellence, disaster recovery, and production readiness.
• Experience leveraging AI-assisted engineering tools such as GitHub Copilot, Amazon Q, OpenAI, Claude, Cursor, or similar technologies to improve developer productivity, automation, troubleshooting, and operational efficiency.
• Familiarity with enterprise AI/LLM concepts including Prompt Engineering, RAG, Vector Databases, AI Agents, LLMOps, AI Security Guardrails, and Responsible AI practices.
• Collaborate with development, architecture, security, and infrastructure teams to improve platform scalability, security posture, developer experience, and engineering productivity.
• Lead cloud modernization, automation, platform engineering, and DevSecOps initiatives while mentoring engineering teams on best practices.