DevSecOps and AI Engineer

Jobtailor; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Jobtailor Bengaluru invites a skilled DevOps/AI/ML engineer to design and run secure, scalable CI/CD pipelines across applications, data, and AI workloads. You’ll integrate security early in the lifecycle, automate SBOMs, ensure dependency management, and deploy containerized services on Kubernetes with Helm or Kustomize.

You will architect and automate end-to-end MLOps workflows, enable AI model training, packaging, and deployment, and build RAG/AI pipelines with vector databases while

Qualifications

• 4-7 years of hands-on experience in cloud, DevOps, and AI/ML workflows.
• Strong skills in Terraform, Kubernetes, Helm, Docker, and CI/CD tools including GitHub Actions, GitLab CI, Jenkins, and Azure DevOps.
• Proficiency in Python and scripting with Bash/PowerShell.
• Experience implementing DevSecOps practices including SAST/DAST, container scanning, secrets scanning, SBOM, and policy-as-code.
• Exposure to MLOps/AI integration using MLflow, Kubeflow, SageMaker, Azure ML, KServe, or Seldon.
• Familiarity with AWS, Azure, or GCP; Ansible/Puppet; and Argo CD/Flux.
• Strong communication, troubleshooting, and collaboration skills.
• B.Tech. / BS in Computer Science
• Knowledge of Terraform, Pulumi, Kubernetes, Docker/Podman, Helm, Kustomize, CI/CD, AWS/Azure/GCP, Python, Bash, PowerShell, DevSecOps tools, policy-as-code, SBOM, MLOps, model serving, vector databases, RAG, monitoring, observability, configuration management, GitOps, and serverless technologies

Responsibilities

• Build and maintain secure CI/CD pipelines for applications, data, and AI workloads.
• Integrate DevSecOps practices with continuous security scanning.
• Implement shift-left security, secret scanning, SBOM automation, and dependency management.
• Deploy containerized workloads on Kubernetes using Helm/Kustomize and secure image pipelines.
• Develop and automate MLOps workflows for model training, packaging, and deployment.
• Build and maintain RAG/AI integration pipelines with vector databases.
• Implement scalable AI inference and model-serving systems.
• Automate ETL/ELT and data feature pipelines for AI model data feeds.
• Provision cloud and AI infrastructure using infrastructure-as-code tools.
• Implement event-driven architectures with serverless functions and messaging systems.
• Maintain monitoring, logging, and model/data observability for application and ML workloads.
• Secure cloud environments using IAM, workload identities, and least-privilege controls.
• Support configuration management and environment automation.
• Develop Python, Bash, or SQL scripts for automation, data processing, validation, and ML workflow orchestration.
• Implement REST, gRPC, or GraphQL API integrations for AI systems.
• Use GitOps tools for secure Kubernetes deployments and progressive delivery.
• Apply AI security practices including guardrails, prompt protection, model validation, and safe inference.
• Ensure compliance with data governance, privacy, GDPR, CCPA, and cloud security standards.
• Collaborate with data engineering, ML engineering, DevOps, and security teams; contribute to documentation, reviews, and mentoring

Skills

CI/CD Pipeline Development

DevSecOps Implementation

MLOps Workflow Automation

Education

B.Tech./BS in Computer Science

Tools

Terraform

Kubernetes

Python

Bash

CI/CD Tools

DevSecOps Practices

MLOps

Containerization

API Integration

Infrastructure-as-Code

Job description

• Build and maintain secure CI/CD pipelines for application, data, and AI workloads
• Integrate DevSecOps practices with continuous security scanning
• Implement shift-left security, secret scanning, SBOM automation, and dependency management
• Deploy containerized workloads on Kubernetes using Helm/Kustomize and secure image pipelines
• Develop and automate MLOps workflows for model training, packaging, and deployment
• Build and maintain RAG/AI integration pipelines with vector databases
• Implement scalable AI inference and model-serving systems
• Automate ETL/ELT and data feature pipelines for AI model data feeds
• Provision cloud and AI infrastructure using infrastructure-as-code tools
• Implement event-driven architectures with serverless functions and messaging systems
• Maintain monitoring, logging, and model/data observability for application and ML workloads
• Secure cloud environments using IAM, workload identities, and least-privilege controls
• Support configuration management and environment automation
• Develop Python, Bash, or SQL scripts for automation, data processing, validation, and ML workflow orchestration
• Implement REST, gRPC, or GraphQL API integrations for AI systems
• Use GitOps tools for secure Kubernetes deployments and progressive delivery
• Apply AI security practices including guardrails, prompt protection, model validation, and safe inference
• Ensure compliance with data governance, privacy, GDPR, CCPA, and cloud security standards
• Collaborate with data engineering, ML engineering, DevOps, and security teams; contribute to documentation, reviews, and mentoring

Requirements

• 4-7 years of hands-on experience in cloud, DevOps, and AI/ML workflows
• Strong skills in Terraform, Kubernetes, Helm, Docker, and CI/CD tools including GitHub Actions, GitLab CI, Jenkins, and Azure DevOps
• Proficiency in Python and scripting with Bash/PowerShell
• Experience implementing DevSecOps practices including SAST/DAST, container scanning, secrets scanning, SBOM, and policy-as-code
• Exposure to MLOps/AI integration using MLflow, Kubeflow, SageMaker, Azure ML, KServe, or Seldon
• Familiarity with AWS, Azure, or GCP; Ansible/Puppet; and Argo CD/Flux
• Strong communication, troubleshooting, and collaboration skills
• B.Tech. / BS in Computer Science
• Knowledge of Terraform, Pulumi, Kubernetes, Docker/Podman, Helm, Kustomize, CI/CD, AWS/Azure/GCP, Python, Bash, PowerShell, DevSecOps tools, policy-as-code, SBOM, MLOps, model serving, vector databases, RAG, monitoring, observability, configuration management, GitOps, and serverless technologies

Core Competencies

Demonstrates expertise in building and maintaining secure CI/CD pipelines, integrating DevSecOps practices, and automating MLOps workflows. Proficient in cloud infrastructure provisioning and implementing scalable AI systems while ensuring compliance with data governance and security standards.

Highest-signal resume keywords

• CI/CD Pipeline Development
• DevSecOps Implementation
• MLOps Workflow Automation

ATS Optimization Keywords

Hard Skills

• Terraform
• Kubernetes
• Python
• Bash
• CI/CD Tools
• DevSecOps Practices
• MLOps
• Containerization
• API Integration
• Infrastructure-as-Code

Soft Skills

• Communication
• Troubleshooting
• Collaboration

Certifications & Qualifications

• B.Tech.
• BS in Computer Science

Industry Keywords

• DevSecOps
• MLOps
• Data Governance
• GDPR
• CCPA
• Monitoring
• Observability
• Configuration Management
• Serverless Technologies
• Vector Databases

Tools & Technologies

• GitHub Actions
• GitLab CI
• Jenkins
• Azure DevOps
• AWS
• Azure
• GCP
• Ansible
• Puppet
• Argo CD