CI / CD Engineer

We are seeking a highly skilled CI/CD Engineer to design, implement, and support end-to-end automation pipelines across application development, data/ML workflows, and AI/LLM deployments. The role includes ownership of integration and deployment services , embedding security-first (DevSecOps) practices, and extending governance to MLOps and LLM SecOps ecosystems. Key Responsibilities: Design, develop, and maintain scalable backend and full-stack applications Build RESTful APIs and microservices using Node.js, TypeScript, Python, and Java Write clean, efficient, and maintainable code following best practices Collaborate with cross-functional teams (DevOps, QA, Product) to deliver high-quality solutions Participate in system design discussions and contribute to architecture decisions Optimize application performance, scalability, and reliability Conduct code reviews and ensure adherence to coding standards Debug, troubleshoot, and resolve production issues Work with databases (SQL/NoSQL) for data modeling and integration Contribute to CI/CD pipelines and automated testing frameworks Qualifications Experience: 1. CI/CD Pipeline Engineering Design, build, and maintain scalable CI/CD pipelines for application, data, ML, and AI systems. Automate build, test, integration, and deployment workflows across cloud (Azure/AWS/GCP) and on-prem platforms. Implement multi-stage pipelines (build test security scan deploy monitor). Integrate source control systems (GitHub, GitLab, Azure DevOps). 2. Integration Deployment Services Support enterprise integration services (APIs, microservices, event-driven architectures). Develop and maintain deployment strategies: Blue/Green deployments Canary releases Feature toggles Manage containerized deployments (Docker, Kubernetes). Ensure environment consistency using IaC tools (Terraform, Bicep, ARM). 3. DevSecOps Implementation Embed security controls in CI/CD pipelines: SAST, DAST, SCA, Container scanning Secrets scanning and credential management Implement policy-as-code and compliance automation. Integrate tools like: SonarQube, Checkmarx Aqua, Prisma Cloud, Trivy Ensure compliance with security standards (ISO, SOC2, GDPR). 4. MLOps / ML SecOps Build and maintain ML pipelines for: Model training, validation, deployment, monitoring Enable ML lifecycle automation (data model deployment retraining). Integrate tools such as: MLflow, Kubeflow, Azure ML, SageMaker Apply ML security practices: Data integrity checks Model drift detection Adversarial robustness validation Ensure reproducibility and traceability of ML experiments. 5. LLM SecOps (AI Governance Security) Implement secure deployment pipelines for LLM-based applications. Monitor and enforce controls for: Prompt injection risks Data leakage sensitive information exposure Model misuse / hallucination tracking Enable AI model governance: Versioning, audit trails, explainability Integrate LLM observability tools (prompt monitoring, response evaluation). Apply Responsible AI practices (bias detection, fairness, compliance). 6. Monitoring Reliability Engineering Implement observability frameworks using: Prometheus, Grafana Azure Monitor, ELK Stack Track: Pipeline health Deployment success rates Security posture Ensure high availability and resilience of CI/CD systems. 7. Collaboration Stakeholder Management Work closely with: Development teams Data scientists / ML engineers Security teams Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.