DevSecOps and AI Engineer-SENIOR

DevSecOps & AI Engineer

Job Description

• Build and maintain secure CI/CD pipelines using GitHub Actions GitLab CI Jenkins Azure DevOps and CircleCI for application data and AI workloads.
• Integrate DevSecOps practices into pipelines using Snyk SonarQube Checkmarx Trivy Anchore and OWASP tools for continuous security scanning.
• Implement shift-left security with secret scanning (GitLeaks TruffleHog) SBOM automation (Syft CycloneDX) and dependency management (Dependabot Renovate).
• Work with containerization (Docker/Podman) and Kubernetes (EKS AKS GKE) including Helm/Kustomize for deployments and secure image pipelines.
• Develop and automate MLOps workflows using MLflow Kubeflow Azure ML SageMaker or Vertex AI for model training packaging and deployment.
• Build and maintain RAG/AI integration pipelines using LangChain LlamaIndex Semantic Kernel and vector databases like Pinecone Weaviate or FAISS.
• Implement AI inference systems using Seldon Core KServe BentoML Ray Serve or Triton Inference Server for scalable model serving.
• Automate ETL/ELT and data feature pipelines using Airflow Prefect Dagster dbt or Kafka/Kinesis for AI model data feeds.
• Work with IaC tools such as Terraform Pulumi CloudFormation or Azure Bicep to provision cloud and AI infrastructure.
• Implement event-driven architectures using serverless functions (AWS Lambda Azure Functions Cloud Functions) and messaging systems like Kafka or RabbitMQ.
• Maintain monitoring and logging using Prometheus Grafana ELK/Loki OpenTelemetry Jaeger Datadog or New Relic for both app and ML workloads.
• Handle model & data observability using tools like Evidently AI Arize AI WhyLabs or Fiddler for drift bias and performance tracking.
• Secure cloud environments using IAM best practices (AWS IAM Azure AD/Entra ID GCP IAM) workload identities and least-privilege controls.
• Support configuration management using Ansible Chef or SaltStack for environment consistency and automation.
• Develop scripts in Python Bash or SQL for automation data processing validation and orchestration of ML workflows.
• Implement API integrations for AI systems using REST gRPC or GraphQL for model consumption and downstream applications.
• Use GitOps tools like Argo CD or Flux for automated secure Kubernetes deployments and progressive delivery.
• Apply AI security practices including guardrails prompt protection model validation and safe inference techniques using industry tools.
• Ensure compliance with data governance privacy and security standards including GDPR CCPA and cloud security best practices.
• Collaborate with data engineers ML engineers DevOps teams and security teams contributing to documentation reviews and mentoring juniors.

Desired Profile

• Looking for a DevSecOps & AI Engineer with 47 years of handson experience in cloud DevOps and AI/ML workflows.
• Strong skills in Terraform Kubernetes Helm Docker and CI/CD (GitHub Actions GitLab CI Jenkins Azure DevOps).
• Proficient in Python and scripting (Bash/PowerShell) with good automation mindset.
• Experience implementing DevSecOps practicesSAST/DAST container scanning secrets scanning SBOM and policy-as-code.
• Exposure to MLOps/AI integration using MLflow Kubeflow SageMaker Azure ML KServe or Seldon.
• Familiar with cloud (AWS/Azure/GCP) configuration management (Ansible/Puppet) and GitOps tools (Argo CD/Flux).
• Strong communication troubleshooting and collaboration skills with ability to work crossfunctionally.

Experience

• 4 to 7 years

Education

• . / BS in Computer Science

Technical Skills & Certifications

• Terraform Pulumi and Infrastructure as Code (IaC)
• Kubernetes (EKS/AKS/GKE) Docker/Podman Helm Kustomize
• CI/CD tools: GitHub Actions GitLab CI Jenkins Azure DevOps
• Cloud platforms: AWS Azure GCP
• Python Bash PowerShell scripting
• DevSecOps tools: Snyk SonarQube Trivy Checkmarx GitLeaks TruffleHog
• Policy-as-code (OPA/Gatekeeper Kyverno) and SBOM tools (Syft CycloneDX)
• MLOps tools: MLflow Kubeflow SageMaker Azure ML Vertex AI
• Model serving frameworks: KServe Seldon Core BentoML Ray Serve
• Vector DBs & RAG stack: Pinecone Weaviate FAISS Chroma LangChain LlamaIndex
• Monitoring & observability: Prometheus Grafana ELK/Loki OpenTelemetry Jaeger
• Configuration management: Ansible Puppet
• GitOps: Argo CD Flux
• Serverless: AWS Lambda Azure Functions Google Cloud Functions.