DevSecOps AI Automation Engineer

The Enterprise; Hyderabad; Presencial; INR 1.500.000 - 2.000.000; Jornada completa

Descripción de la vacante

The Enterprise is seeking a DevSecOps AI Automation Engineer in Hyderabad, India, to build AI-native tools for software delivery pipelines. This role involves designing AI-augmented CI/CD workflows and integrating security tooling. Candidates should have over 5 years of experience in DevOps or DevSecOps and a strong understanding of CI/CD processes, scripting in Python or Go, and security integration.

The ideal candidate will be familiar with LLM APIs and have experience with infrastructure-as-code tools like Terraform. A background in application security is a plus.

Formación

• 5+ years of experience in DevOps, platform engineering, or DevSecOps roles.
• Hands-on experience operating CI/CD pipelines at enterprise scale.
• Practical experience integrating security tooling into automated pipelines.

Responsabilidades

• Design and implement AI-augmented CI/CD pipelines.
• Build LLM-powered tools that integrate into GitHub Actions and other CI services.
• Automate security workflows and own observability layers for delivery pipelines.

Conocimientos

DevOps experience

CI/CD pipelines

Python or Go scripting

Security tooling integration

Infrastructure-as-code

LLM APIs familiarity

Descripción del empleo

DevSecOps AI Automation Engineer

Bring AI-native intelligence to the software delivery pipeline, automated security gates, release risk scoring, and self‑healing CI/CD for enterprise engineering teams.

About the role

Software delivery pipelines generate enormous signal, build failures, security findings, code review patterns, deployment anomalies and most enterprise teams are too overwhelmed to act on all of it. ArqRelease is ArqAI's answer: an AI-native layer that sits inside the CI/CD pipeline and turns noise into decisions. You'll build the LLM-powered tools that make this real release risk classifiers, automated vulnerability triage, code review summaries, post‑deployment anomaly detection, integrated directly into the workflows engineers already use. This is a role for someone with deep DevSecOps foundations who's genuinely excited about what AI can do to the delivery lifecycle, not someone who wants to write AI wrappers without understanding what they're wrapping.

What you'll do

Design and implement AI‑augmented CI/CD pipelines: automated code review summaries, security finding triage, release risk classification, and post‑deployment anomaly detection.

Build LLM-powered tools that integrate into GitHub Actions, GitLab CI, Jenkins, or Azure DevOps, surfacing actionable intelligence at the right point in the developer workflow.

Automate security workflows: SAST/DAST result enrichment, dependency vulnerability prioritisation, secret detection, and policy‑as‑code enforcement with AI‑assisted remediation suggestions.

Own the observability layer for AI‑augmented delivery pipelines: metrics, alerts, drift detection, and rollback triggers that engineering teams can trust.

Work with customer platform and security teams to define acceptable automation boundaries, what the AI recommends, what it gates, and what always requires a human decision.

What we're looking for

5+ years in DevOps, platform engineering, or DevSecOps roles with hands‑on experience operating CI/CD pipelines at enterprise scale.

Practical experience integrating security tooling (Snyk, Veracode, SonarQube, Trivy, or comparable) into automated pipelines, not just running scans manually.

Python or Go for scripting and tooling; comfortable working with REST APIs, webhooks, and event‑driven pipeline architectures.

Familiarity with LLM APIs and prompt engineering, enough to build workflow integrations that produce useful, structured outputs engineers will act on.

Experience with infrastructure‑as‑code (Terraform, Pulumi, or CDK) and container/Kubernetes‑based deployment environments.

Nice To Have

• Background in application security (OWASP, penetration testing fundamentals, secure SDLC) alongside the DevOps background.
• Experience building internal developer platforms or golden‑path tooling that engineering teams actually adopt.
• Familiarity with supply chain security standards: SBOM generation, SLSA levels, Sigstore, or similar.