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This role focuses on DevSecOps and AI Engineering, building and maintaining secure CI/CD pipelines, integrating advanced security practices, and developing robust MLOps and AI integration workflows. You'll work with cutting-edge technologies in containerization, orchestration, and AI model deployment to ensure secure, scalable, and efficient operations.

Design, build, and manage secure CI/CD pipelines using tools like GitHub Actions, GitLab CI, Jenkins, and Azure DevOps for diverse workloads including applications, data, and AI. Integrate DevSecOps practices by implementing continuous security scanning with tools such as Snyk, SonarQube, and OWASP. Shift-left security by automating secret scanning, SBOM generation, and dependency management.

Implement containerization with Docker/Podman and Kubernetes (EKS, AKS, GKE), managing deployments with Helm/Kustomize. Develop and automate MLOps workflows using MLflow, Kubeflow, or Azure ML for model training and deployment. Build RAG/AI integration pipelines with LangChain, LlamaIndex, and vector databases. Deploy AI inference systems using KServe or Seldon Core for scalable model serving.

Automate ETL/ELT and data feature pipelines with Airflow, Prefect, or dbt. Provision cloud infrastructure using IaC tools like Terraform or Pulumi. Implement event-driven architectures with serverless functions and messaging systems. Maintain comprehensive monitoring and logging using Prometheus, Grafana, or ELK stack. Handle model and data observability with tools like Evidently AI or Arize AI to track drift and performance. Secure cloud environments using IAM best practices and least-privilege controls. Support configuration management with Ansible or Chef. Develop automation scripts in Python, Bash, or SQL. Implement API integrations for AI systems and use GitOps tools like Argo CD for automated deployments. Apply AI security practices and ensure compliance with data governance and privacy standards.

This DevSecOps & AI Engineer position requires 4–7 years of hands-on experience in cloud, DevOps, and AI/ML workflows. Strong proficiency is essential in Terraform, Kubernetes, Helm, Docker, and CI/CD tools such as GitHub Actions, GitLab CI, Jenkins, and Azure DevOps. A solid command of Python and scripting (Bash/PowerShell) with a strong automation mindset is expected.

Demonstrated experience in implementing DevSecOps practices, including SAST/DAST, container scanning, secrets scanning, SBOM, and policy-as-code is crucial. Familiarity with MLOps/AI integration tools like MLflow, Kubeflow, SageMaker, Azure ML, KServe, or Seldon is beneficial. Experience with major cloud platforms (AWS/Azure/GCP), configuration management tools (Ansible/Puppet), and GitOps tools (Argo CD/Flux) is required.

Excellent communication, troubleshooting, and collaboration skills are necessary to work effectively across cross-functional teams. A Bachelor's degree (B.Tech. / BS) in Computer Science or a related field is required.