We are looking for an experienced Expert AI Engineer to join an international technology project and help build and scale an internal AI platform used by both engineering and non-technical teams.
This is a hands-on role for an engineer who has already built and delivered production-grade AI tools, AI agents, integrations, and automation solutions. You will work on AI infrastructure, agentic workflows, MCP integrations, security, governance, and organization-wide AI enablement.
What You’ll Do
- Design, build, deploy, and maintain internal AI platforms and AI-powered tools.
- Develop custom AI skills, MCP connectors, integrations, and agentic workflows.
- Build scalable infrastructure for hosting AI tools, model integrations, and automated execution pipelines.
- Design and implement multi-agent workflows and orchestration pipelines for complex engineering and business processes.
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Ensure secure AI implementation, including:
- credential and secrets management;
- least-privilege access;
- prompt injection protection;
- prevention of sensitive data leakage.
- Integrate AI solutions with existing engineering and business systems.
- Establish standards and best practices for AI governance, security, quality, and reliability.
- Optimize LLM usage and infrastructure costs using techniques such as model routing, semantic caching, and usage controls.
- Support engineering teams in adopting AI tools and integrating them into daily development workflows.
- Work with cross-functional stakeholders to identify opportunities where AI can improve productivity and delivery.
- Define metrics to evaluate the impact of AI solutions on engineering efficiency and product delivery.
The original role specifically focuses on company-wide AI tooling rather than work with one particular framework or programming language.
Requirements
- 10+ years of professional software development experience.
- Strong background in backend engineering, software architecture, and system design.
- Strong hands-on experience building and shipping production AI solutions.
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Practical experience with:
- AI agents and agentic workflows;
- MCP / Model Context Protocol integrations;
- custom AI tools or skills;
- LLM-based applications and integrations.
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Strong backend development experience with at least one modern language such as:
- Python;
- TypeScript / Node.js;
- Go;
- Java;
- or another comparable backend technology.
- Experience designing and managing cloud infrastructure using AWS, GCP, or Azure.
- Strong understanding of AI security, including credential management, access controls, prompt injection risks, and sensitive data protection.
- Experience supporting or leading AI adoption within engineering teams.
- Ability to translate technical AI concepts into practical solutions aligned with business needs.
- Excellent communication and leadership skills.
- English: Advanced/ Native-level proficiency.
These requirements follow the source profile, including the 10+ years of engineering background, language-agnostic backend requirement, production MCP/agentic tooling, cloud infrastructure, AI security, and C2/native English.
Nice to Have
- Experience with AI FinOps / LLM cost optimization, including semantic caching, rate limiting, and cost-aware model routing.
- Knowledge of MCP standards and specifications.
- Experience with frameworks such as LangChain or LlamaIndex.
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Experience with:
- Docker;
- Terraform / Infrastructure as Code;
- CI/CD;
- observability and monitoring tools such as Datadog or ELK.
About the Project
You will join an international technology environment focused on building secure digital solutions used at scale. The role has a strong emphasis on security, AI enablement, automation, and internal developer productivity.
You will have the opportunity to influence how AI is adopted across the organization and build tools that are used not only by software engineers but also by other teams across the business. The underlying project operates across Europe and the US and serves millions of users and thousands of enterprise customers.
Working Environment
- International team and global product environment.
- Opportunity to work on large-scale, production AI systems.
- Significant technical ownership and influence over AI architecture and standards.
- Collaboration with experienced engineering and product teams.
- Remote-friendly international environment.