Cloud & Infrastructure Expert Full-Stack Engineer

Job Summary

Our Consulting team at BearingPoint is dedicated to optimizing client business operations through advanced ERP and CRM systems. With deep expertise in SAP across various domains like finance, logistics, data, and technology, we streamline processes and enhance decision-making efficiency. Our proficiency in Salesforce helps us improve customer relationships with innovative strategies. We leverage data tools like PowerBI for insightful analytics and excel in Microsoft cloud technologies, particularly Azure, to provide secure and scalable solutions. Additionally, our team develops integrated mobile front-end and back-end applications, ensuring user-friendly mobile access to ERP and CRM systems.

BearingPoints consulting services are designed to deliver tailored solutions that meet the unique needs of each client, driving growth and operational excellence. We pride ourselves on our client-centric approach, focusing on delivering measurable results and sustainable improvements.

As an Expert Fullstack Engineer focused on infrastructure, you will be a hands-on technical lead. You will design, build and run secure, scalable and resilient Generative AI solutions, including BeMind, BearingPoints enterprise AI platform, from the application down to the cloud infrastructure. About half of your time goes to infrastructure, security and DevOps. The other half goes to backend and full-stack engineering.

Responsibilities

• Architecture & infrastructure: design and build enterprise-grade cloud solutions, with Azure as the focus, that are highly available, secure, cost-efficient and compliant.
• Networking, security & identity: own network design, security and identity and access management (RBAC, MFA, least privilege), and set best practices for the team.
• DevOps & automation: evolve CI/CD pipelines, infrastructure as code and automation of cloud resources, and make releases routine and low-risk.
• Containers & services: containerise applications, evolve architecture from monoliths towards services, and choose the right compute model (App Services, serverless, VMs, Kubernetes).
• AI infrastructure & APIs: run AI services such as Azure OpenAI / AI Foundry and AI Search, and expose, secure and govern APIs, including through API Management and as MCP servers.
• Full-stack engineering: build robust backend APIs and contribute to the frontend where platform knowledge is critical.
• Reliability: set up monitoring, logging and alerting, and drive performance and cost optimisation.
• Leadership: mentor colleagues, set architecture standards and explain complex topics clearly to technical and non-technical stakeholders.

What we are looking for

• Bachelors degree in Computer Science, Engineering or a related field (Masters degree or further cloud training is a plus), or equivalent practical experience.
• Around 8+ years of professional IT experience, including several years building and running production cloud infrastructure and doing backend / full-stack development.
• Deep hands-on experience with at least one major cloud. Azure is ideal; strong AWS or GCP experience is welcome if you are willing to become proficient in Azure quickly.
• Solid cloud networking and security fundamentals: virtual networks, firewalls / NSGs, load balancing, DNS, TLS, identity and access management.
• Infrastructure as code in production (Terraform, Bicep / ARM, Pulumi, CloudFormation or similar).
• Designing CI/CD pipelines in any mainstream tool (GitHub Actions, Azure DevOps, GitLab CI, Jenkins) and container-based deployments with Docker.
• Strong backend skills in at least one language: TypeScript / Node.js is ideal; Python, C# / .NET, Java or Go are welcome. You can also work on the frontend.
• Strong relational database skills, including design and performance tuning. PostgreSQL is preferred.
• Proven experience troubleshooting complex systems and improving reliability in production.
• A track record of delivering enterprise projects from concept to production and of leading teams technically.

Nice to have

• Azure AI Foundry / Azure OpenAI, Azure AI Search or other LLM services in production.
• Azure API Management (policies, authentication mechanisms) and exposing APIs as MCP servers.
• Kubernetes and container orchestration.
• Serverless architectures (Azure Functions, AWS Lambda or similar).
• Leading monolith-to-microservices migrations or cloud transformations.
• Azure Monitor / Log Analytics or comparable observability tooling.
• Consulting or client-facing project experience.
• Certifications such as AZ-305, AZ-104, AZ-204, AZ-400, AZ-500 or AWS / GCP equivalents. A plus, not a requirement.

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.