CI/CD Engineer

Role Overview:

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

• Drive adoption of DevOps culture and best practices.
• Provide support for release management and incident resolution.

Educational Background:

• B.Tech/B.S./ M.S. in Computer Science, Statistics, Mathematics, or a related field

Additional Skills & Preferred Qualifications:

• Strong communication, stakeholder management, and organizational skills
• Self-motivated, customer-focused, and detail-oriented mindset
• Certification in:

• Azure DevOps / AWS DevOps
• Kubernetes (CKA/CKAD)

• Experience with AI governance frameworks
• Background in data engineering or ML engineering
• Experience working in Agile/Scrum environments
• Knowledge of ERP systems (SAP) – strongly preferred
• Six Sigma Yellow Belt or Green Belt certification – a plus
• ITIL certification – a plus

Any Questions 

Mukund.Dighe&mahle.com