Senior Devsecops Engineer (ai/ml Systems)

Autodesk Autodesk · Enterprise · Bangalore, India

Senior DevSecOps Engineer focused on building, securing, and scaling AI/ML platforms and services. The role involves integrating security throughout the ML lifecycle, designing secure cloud-native architectures, automating security controls, and enabling secure AI innovation. Responsibilities include securing AI/ML platforms, model training pipelines, inference services, and AI applications, with a focus on Generative AI, LLMs, RAG, and agent-based systems. The role also emphasizes DevSecOps practices, cloud security, application security, vulnerability management, and monitoring solutions.

What you'd actually do

  1. AI/ML Platform Security
  2. DevSecOps & Security Automation
  3. Cloud & Infrastructure Security
  4. Application Security
  5. Vulnerability Management
  6. Monitoring & Operations

Skills

Required

  • 5+ years of experience in Security Engineering, Application Security, Security Operations, or DevSecOps roles
  • Experience working with cloud platforms such as AWS, Azure, or GCP
  • Experience with Docker, Kubernetes, and cloud-native technologies
  • Strong understanding of secure software development lifecycle (SSDLC)
  • Deep knowledge of Secure Coding Practices, OWASP Top 10, OWASP API Security Top 10, Threat Modeling, Vulnerability Management
  • Hands-on experience with SAST, DAST, SCA, Container Security, Secrets Management
  • Experience automating workflows using Python, Golang, Bash, TypeScript, or equivalent languages
  • Familiarity with CI/CD pipelines and Git-based development workflows
  • Experience designing security architectures that address complex threat models and compliance requirements
  • Strong REST and GraphQL API experience, including authentication, authorization, and API security best practices
  • Excellent communication and stakeholder management skills

Nice to have

  • Experience with AI/ML platforms such as MLflow, Kubeflow, SageMaker, Vertex AI, or Databricks
  • Experience securing Generative AI, LLM applications, AI agents, and RAG architectures
  • Knowledge of MITRE ATLAS
  • NIST AI Risk Management Framework
  • Responsible AI principles
  • Experience with Kubernetes security and cloud-native security platforms.
  • Familiarity with SOC2, ISO 27001, NIST, HIPAA, or GDPR compliance frameworks
  • Security certifications such as CISSP, CCSP, CSSLP, CKS, or AWS Security Specialty
  • Python, TypeScript, Terraform, Kubernetes, Docker, GitHub Actions, GitLab CI, Jenkins, AWS / Azure / GCP, Snyk, SonarQube, Open Telemetry, Prometheus, Grafana, ELK/OpenSearch, Comet Opik

What the JD emphasized

  • AI/ML platforms
  • model training pipelines
  • inference services
  • AI applications
  • Generative AI
  • LLM
  • RAG
  • agent-based systems
  • responsible AI
  • GPU workloads
  • Kubernetes
  • DevSecOps
  • CI/CD pipelines
  • cloud-native architectures
  • AWS, Azure, GCP
  • Terraform
  • OWASP
  • Vulnerability Management
  • Observability
  • OpenTelemetry
  • Prometheus
  • Grafana
  • ELK/OpenSearch

Other signals

  • AI/ML platforms
  • Generative AI
  • LLM
  • RAG
  • agent-based systems
  • responsible AI
  • GPU workloads
  • Kubernetes
  • DevSecOps
  • CI/CD pipelines
  • cloud-native architectures
  • AWS, Azure, GCP
  • Terraform
  • OWASP
  • Vulnerability Management
  • Observability
  • OpenTelemetry
  • Prometheus
  • Grafana
  • ELK/OpenSearch