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CAI

Senior Application Security Engineer

IndiaPosted 6 days ago
Full-timehybrid

Job Description

Senior Application Security Engineer

Req number:

R7815

Employment type:

Full time

Worksite flexibility:

Remote

Who we are

CAI is a global services firm with over 9,000 associates worldwide and a yearly revenue of $1.3 billion+. We have over 40 years of excellence in uniting talent and technology to power the possible for our clients, colleagues, and communities. As a privately held company, we have the freedom and focus to do what is right—whatever it takes. Our tailor-made solutions create lasting results across the public and commercial sectors, and we are trailblazers in bringing neurodiversity to the enterprise.

Job Summary

We are looking for a motivated Senior Application Security Engineer ready to take us to the next level! If you have security architecture reviews, and code security assessments across microservices and cloud-native applications and are looking for your next career move, apply now.

Job Description

We are looking for a Senior Application Security Engineer to security architecture reviews, and code security assessments across microservices and cloud-native applications. This position will be full time and Remote/Hybrid(Bangalore) 

 

What You’ll Do 

  • Application Security Engineer with 5 to 8 years of experience. 

  • Lead threat modeling, security architecture reviews, and code security assessments across microservices and cloud-native applications 

  • Perform SAST, DAST, SCA, and manual penetration testing on applications 

  • Identify, prioritize, and track remediation of vulnerabilities across the application portfolio 

  • Define and enforce secure coding standards and security best practices 

  • AI / LLM Security (AI Transformation Center Focus) 

  • Threat model AI systems — identify and mitigate risks unique to LLM-powered applications: prompt injection, jailbreaking, model inversion, data poisoning, and training data leakage 

  • Conduct adversarial testing (red-teaming) of LLM and generative AI applications before production release 

  • Define and implement guardrails and content safety controls for LLM inputs/outputs (e.g., PII detection, toxicity filtering, output validation. 

  • Evaluate and secure AI supply chain — third-party model APIs (OpenAI, Anthropic, Azure OpenAI), open-source model weights (Hugging Face), and fine-tuned models 

  • Establish policies around RAG (Retrieval-Augmented Generation) security — securing vector databases, embedding pipelines, and document ingestion workflows 

  • Implement controls against indirect prompt injection in agentic AI systems and multi-step LLM pipelines 

  • Assess AI agent security — tool call authorization, agent sandboxing, privilege boundaries, and action scope controls 

  • Ensure compliance with AI governance frameworks: ISO/IEC 42001, and internal AI usage policies 

  • Collaborate with Data Science and ML Engineering teams to secure model training pipelines, dataset storage, and model registries 

  • Drive AI incident response playbooks specific to model abuse, hallucination exploitation, and data exfiltration via LLM interfaces 

What You'll Need 

 

Required: 

  • DevSecOps & CI/CD Security 

  • Design and maintain DevSecOps pipelines integrating security gates into GitHub Actions workflows 

  • Automate SAST, container scanning, secrets detection, SCA, and AI model scanning within CI/CD pipelines 

  • Implement and manage Argo CD security policies, RBAC configurations, and deployment guardrails for GitOps workflows 

  • Enforce branch protection, signed commits, and secrets management in GitHub 

  • Integrate ML model integrity checks and artifact signing into AI deployment pipelines 

  • Container & Orchestration Security 

  • Harden Docker images — least-privilege, minimal base images, multi-stage builds, and image signing 

  • Secure Kubernetes clusters: RBAC, Pod Security Admission, Network Policies, OPA/Gatekeeper, and runtime security 

  • Isolate and sandbox AI inference workloads in Kubernetes — GPU node security, model server hardening (Triton, TorchServe, vLLM) 

  • Integrate container vulnerability scanning (Trivy, Grype, Snyk) into pipelines 

  • Security Operations & Governance 

  • Define security benchmarks aligned to ISO42001, NIST, OWASP (including OWASP LLM Top 10), and internal standards 

  • Collaborate with AI Engineering TEAM, Security and Risk Management Team, GRC Team, Infosec Team to shift security left 

 

Physical Demands 

  • Ability to safely and successfully perform the essential job functions 

  • Sedentary work that involves sitting or remaining stationary most of the time with occasional need to move around the office to attend meetings, etc. 

  • Ability to conduct repetitive tasks on a computer, utilizing a mouse, keyboard, and monitor 

 

Reasonable accommodation statement

If you require a reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employment selection process, please direct your inquiries to [email protected] or (888) 824 – 8111.

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Senior Application Security Engineer at CAI | Renata