YOUR ROLE

CI/CD Engineer -

 Role Overview

We are seeking a skilled CI/CD 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.