We are seeking a skilled Senior DevSecOps Engineer to design, implement, and support end-to-end automation pipelines across application, data/ML, and AI/LLM deployments. The role includes ownership of integration and deployment services, embedding DevSecOps practices and extending governance to MLOps and LLM SecOps.
Key Responsibilities
5-8 years in DevOps, CI/CD Engineering, Platform Engineering, or SRE.
Proven experience designing and maintaining enterprise CI/CD pipelines across application and cloud-native environments.
Hands-on with CI/CD platforms: Azure DevOps, GitHub Actions, GitLab CI, or Jenkins.
Experience with IaC (Terraform, Bicep, ARM, or CloudFormation).
Experience deploying and managing containerized apps using Docker and Kubernetes.
DevSecOps practices: security scanning, secrets management, policy enforcement.
MLOps experience: model deployment, versioning, monitoring, ML lifecycle automation.
Experience with cloud platforms (Azure, AWS, or GCP) in enterprise environments.
Collaboration with development, Data Engineering, ML Engineering, and security teams in Agile settings.
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 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 deployment strategies: Blue/Green, Canary releases, Feature toggles. Manage containerized deployments. Ensure environment consistency using IaC tools.
3. DevSecOps Implementation — Embed security controls in CI/CD pipelines: SAST, DAST, SCA, container scanning, secrets scanning, credential management. Implement policy-as-code and compliance automation. Integrate tools like SonarQube, Checkmarx, Aqua, Prisma Cloud, Trivy. Ensure compliance with ISO, SOC2, GDPR standards.
4. MLOps / ML SecOps — Build and maintain ML pipelines for model training, validation, deployment, and monitoring. Enable ML lifecycle automation. 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, and model misuse/hallucination tracking. Enable AI model governance: versioning, audit trails, explainability. Integrate LLM observability tools. 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, and security posture. Ensure high availability and resilience of CI/CD systems.
7. Collaboration & Stakeholder Management — Work closely with development teams, data scientists/ML engineers, and security teams. Drive adoption of DevOps culture and best practices. Support release management and incident resolution.
YOUR PROFILE
Educational Background
B.Tech/B.S./M.S. in Computer Science, Statistics, Mathematics, or related field.
Additional Skills & Preferred Qualifications
Strong communication, stakeholder management, and organizational skills. Self-motivated, customer-focused, detail-oriented mindset. Certifications in Azure/AWS DevOps, Kubernetes (CKA/CKAD) preferred. Experience with AI governance frameworks. Background in data or ML engineering. Agile/Scrum experience. Knowledge of SAP ERP systems strongly preferred. Six Sigma or ITIL certification is a plus.