Senior Enterprise Cloud Software Engineer (Cloud Operations, AI Engineering, Azure & AWS)

Mission of the Role

• Transform Cloud Operations from reactive support into proactive, software‑defined platforms
• Fuse software engineering rigor with operational ownership
• Leverage automation, Infrastructure‑as‑Code, and AI to eliminate manual work at scale
• Design systems that are secure, observable, resilient, and self‑optimizing by default
• Act as a technical bridge between Engineering, Cloud Operations, Security, and Architecture

Technical Skills & Competencies

Enterprise Cloud Software Engineering

• Design and build enterprise‑grade cloud services, APIs, and automation platforms using a Python‑first stack (FastAPI/Django/Flask), with UI components where needed.
• Develop cloud‑native tooling and services using Azure and AWS SDKs to standardize provisioning, lifecycle management, and operational workflows.
• Apply modern software engineering practices: clean architecture, SOLID principles, automated testing, and CI/CD‑driven delivery.
• Design contract‑first APIs (REST/GraphQL) and event‑driven services to enable platform extensibility and integration.

Cloud Operations Engineering (Software‑Defined)

• Own the full lifecycle of cloud platforms: design, build, operate, optimize, and decommission.
• Engineer automation‑first infrastructure operations, eliminating manual provisioning, patching, scaling, and recovery.
• Implement Infrastructure‑as‑Code frameworks using Terraform (modules, state management, drift detection) and supporting tools (Ansible, ARM/Bicep, CloudFormation).
• Build and operate high‑availability, fault‑tolerant, and disaster‑resilient architectures across hybrid and multi‑cloud environments.
• Lead L3/L4 troubleshooting, root‑cause analysis, and reliability improvements for complex cloud incidents.

AI‑Driven & Intelligent Operations

• Apply AI‑assisted CloudOps practices including anomaly detection, predictive insights, event correlation, and automated remediation.
• Design and operate intelligent runbooks and AI agents that execute operational decisions safely and audibly.
• Integrate approved GenAI and ML services into operational workflows while adhering to governance and responsible AI standards.
• Use AI tools to accelerate development, testing, documentation, and operational analysis.

DevOps, CI/CD & Governance

• Design and maintain CI/CD pipelines for infrastructure and platform automation using Azure DevOps, GitHub Actions, or GitLab.
• Implement policy‑as‑code, security guardrails, tagging standards, and cost‑optimization controls.
• Embed observability, APM, logging, and SLO/SLA practices into platforms from day one.
• Ensure platforms are built to be operable by design, not retrofitted for operations.

Leadership & Engineering Influence

• Serve as a technical leader and mentor, elevating engineering and CloudOps maturity.
• Collaborate closely with Architecture, Security, Compliance, and Product teams.
• Establish standards, patterns, documentation, and reusable frameworks used across the enterprise.
• Drive continuous improvement, reliability engineering, and operational excellence.

Required Qualifications

Education:

• Required: Bachelor's degree in Computer Science, or a related field

Experience

• More than 5 years of Cloud Operations, DevOps, or Infrastructure Engineering
• Hands-on experience in AI-based automation is highly desirable.
• Proven experience operating large-scale AWS and Azure environments

Certifications:

• Advanced certification in AWS and Azure will be preferred.
• Terraform Associate
• AI or ML Certification

Core Technical Expertise

• Deep hands‑on experience in AWS and Azure at enterprise scale
• Strong proficiency in Python and scripting for automation and platform development
• Advanced Terraform expertise with modular, reusable, enterprise‑grade designs
• Experience with containers and orchestration (Docker, Kubernetes, AKS, EKS)
• Strong understanding of networking, identity, security, and cloud governance
• Experience with CI/CD, release management, and platform operationalization

AI & Advanced Automation

• Practical experience applying AI to operational workflows
• Understanding of AI fundamentals, safe usage, and lifecycle governance
• Ability to design AI‑assisted automation without compromising reliability or compliance

Engineering Mindset

• Strong architectural thinking and systems design skills
• Passion for eliminating toil through code
• Ability to operate independently while influencing across teams
• Excellent communication and documentation skills

Our Interview Practices

To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.

Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.