Python /AI developer
Cloud Power LuxembourgDescription du poste
We are looking for a Python / AI Developer to join a team responsible for the operation and continuous evolution of an enterprise AI platform within a regulated banking environment.
The platform provides secure and governed access to Large Language Models, knowledge bases, enterprise search, conversational agents, RAG services, evaluation frameworks and administration tools.
This is a hands-on role combining software engineering, AI platform engineering, production support, security and technical governance.
Your responsibilities
- Maintain and enhance the AI platform, including Python/FastAPI services, agent engines, connectors, policy controls, databases, background processes and React administration interfaces.
- Operate and improve enterprise search and RAG integrations with SharePoint, intranet and other authorised data sources.
- Develop and maintain AI evaluation frameworks covering quality, groundedness, safety, regression, latency, token usage and cost.
- Implement safeguards against prompt injection, data leakage, unsafe tool execution and inappropriate handling of confidential or personal information.
- Investigate production incidents and improve reliability, performance, scalability, observability and cost efficiency.
- Maintain secure authentication and access controls using OIDC/OAuth2, JWT, RBAC, tenant isolation and resource policies.
- Manage PostgreSQL data models, migrations, transactions and data integrity.
- Support Docker/Kubernetes deployments, Helm, Terraform, CI/CD and cloud infrastructure.
- Implement monitoring, structured logging, metrics, tracing and alerting.
- Develop automated tests covering APIs, databases, security, integrations and user interfaces.
- Produce technical documentation, architecture diagrams, runbooks and operating procedures.
- Participate in code, architecture, security and release-readiness reviews.
- Contribute to knowledge sharing and coaching of internal teams.
- 5+ years of relevant experience in software engineering, platform engineering or a related technical role.
- Strong hands-on experience with Python, asynchronous programming, FastAPI, Pydantic and typed application design.
- Strong knowledge of PostgreSQL, SQL, database design, migrations, transactions, concurrency and data integrity.
- Practical experience with LLMs, AI agents, RAG, embeddings, enterprise search, prompt engineering and tool calling.
- Experience designing and implementing automated AI evaluation frameworks.
- Good understanding of AI security risks, including prompt injection, retrieved-content attacks, data leakage and unsafe tool execution.
- Strong knowledge of API security, OIDC/OAuth2, JWT, access control, secrets management and secure development practices.
- Experience with Docker, Kubernetes, Helm, Terraform, CI/CD and Azure or a comparable enterprise cloud platform.
- Experience with observability tools such as OpenTelemetry, Splunk or Prometheus.
- Strong automated-testing capabilities.
- Working knowledge of TypeScript and React.
- Professional proficiency in English.
- Experience with Azure OpenAI, Azure AI Search, Microsoft Graph or SharePoint.
- Knowledge of AI model and agent lifecycle governance.
- Experience in banking, financial services, EU institutions or another regulated environment.
- Knowledge of GDPR, the EU AI Act, DORA or similar regulatory frameworks.
- Experience with performance testing, capacity planning, disaster recovery and service continuity.
- Knowledge of Model Context Protocol (MCP) or similar tool-integration standards.
- Experience with Temporal or other workflow orchestration technologies.
- Experience with multilingual enterprise applications.
- French language skills.
You value secure, maintainable and supportable solutions and are able to explain complex AI concepts to both technical and non-technical stakeholders.
You will contribute to
- Secure and reliable operation of the AI platform.
- Timely resolution of incidents, vulnerabilities and operational risks.
- Controlled delivery of tested and documented platform improvements.
- Continuous improvement of AI evaluation benchmarks and release-readiness processes.
- Up-to-date architecture documentation, runbooks, data models and support procedures.
- Monitoring and reporting of reliability, security, AI quality, usage, cost and technical debt.
- Effective knowledge transfer to enable internal teams to operate and evolve the platform sustainably.
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