Sr. AI Developer Engineer – DevSecOps Tools (L3)

Theomnihire; Mumbai; On-site; INR 3,000,000 - 5,000,000; Full time

Job summary

Theomnihire in Mumbai is seeking a Sr. AI Developer Engineer – DevSecOps Tools to build AI-powered security automation that embeds protection into CI/CD pipelines and the software delivery lifecycle.

You will implement ML-driven anomaly detection, LLM-based IaC remediation, and contextual in-pipeline guidance, while maintaining production-grade code and automations as an individual contributor.

Qualifications

• 4–6 years of AI/ML development, DevSecOps, or security automation experience.
• B.Tech or M.Tech in CS/IT/AI or related technical field.
• Certifications in security are a plus (e.g., CEH, SC-200).

Responsibilities

• CI/CD Security Automation: build AI-powered security automation integrated into CI/CD pipelines to enforce policy-as-code and pre-merge checks.
• Pipeline Anomaly Detection: develop ML models to detect unusual commits and integrity violations before production.
• IaC Remediation Assistants: create LLM-powered remediation for Terraform, ARM, Helm that generates corrected code.
• Contextual In-Pipeline RAG: implement RAG pipelines delivering in-context remediation guidance to developers.
• Agentic Multi-Tool Orchestration: coordinate multi-tool scans and auto‑raise tickets for critical findings.
• Scan Report NLP Pipelines: train NLP models to parse and summarize security scan reports.

Skills

CI/CD Automation

AI/ML Development

Security Automation

SAST/SCA

LLMs/NLP

Terraform/Checkov

Python

Container Security

Education

B.Tech or M.Tech in CS/IT/AI

Tools

GitHub Actions

Jenkins

Azure DevOps

GitLab CI

Terraform

Checkov

Terrascan

LLMs

Job description

Job Title: Sr. AI Developer Engineer – DevSecOps Tools (L3)

Working Hours: 9:00 AM – 6:00 PM

Mode of Interview: Face-to-Face or MS Teams

Position Summary

As part of the DevSecOps Tools vertical, the Sr. AI Developer Engineer – DevSecOps Tools is a hands‑on developer role focused on building AI‑powered automation that embeds security natively into CI/CD pipelines and the software delivery lifecycle. Working under the direction of the Lead Engineer, this role will develop intelligent security scan orchestration, ML-driven pipeline anomaly detection, and LLM‑based remediation tools for IaC and container security. The engineer writes production‑quality code, maintains deployed automations, and continuously improves pipeline security coverage in a pure individual contributor role with no people‑management responsibilities.

Requirements

Key Responsibilities

CI/CD Security Automation: Build AI‑powered security automation that integrates natively into CI/CD pipelines (Azure DevOps, GitHub Actions, Jenkins, GitLab CI) to enforce policy‑as‑code, security gates, and pre‑merge vulnerability checks.

Pipeline Anomaly Detection: Develop ML models to detect pipeline anomalies, flagging unusual code commits, suspicious dependency changes, and build‑time integrity violations before deployment to production.

IaC Remediation Assistants: Build LLM‑powered Infrastructure as Code (IaC) remediation assistants that detect misconfigurations in Terraform, ARM templates, and Helm charts (via Checkov, Terrascan) and auto‑generate corrected code snippets.

Contextual In-Pipeline RAG: Implement RAG pipelines over internal security policies, hardening baselines, and compliance frameworks to deliver contextual, in‑pipeline remediation guidance directly to software developers.

Agentic Multi-Tool Orchestration: Develop agentic AI workflows that orchestrate multi‑tool security scans (SAST, SCA, container scanning, secrets detection), aggregate scan results, and auto‑raise prioritized tickets for critical findings.

Scan Report NLP Pipelines: Build and fine‑tune NLP models to parse, categorize, and summarize findings across heterogeneous security scan report formats.

Experience: 4–6 years of relevant experience in AI/ML development, DevSecOps engineering, or security automation.

Education: B.Tech or M.Tech in Computer Science, Information Technology, AI/ML, or a related technical field.

Certifications: CompTIA Security+, CEH, CKS (Certified Kubernetes Security Specialist), or SC‑200.

Technical Stack: Python, CI/CD tools (GitHub Actions, Jenkins, Azure DevOps, GitLab CI), IaC tools (Terraform, Checkov), LLMs, RAG, NLP libraries, and container security scanners.