Your Role
As an AI Engineer, you will design, build and deploy AI-driven applications on Azure, working closely with business stakeholders and Solution Designers.
You are expected to be hands-on, pragmatic, and able to move from prototype to production.
Responsibilities
- Design and implement end-to-end AI solutions (from data ingestion to deployment)
- Build and deploy GenAI use cases (RAG, copilots, assistants, document processing)
- Develop and maintain APIs (e.g. FastAPI) integrating AI services into the ecosystem
- Deploy and manage applications on Azure (AKS, Azure ML, Functions, etc.)
- Work with LLMs (Azure OpenAI) and ensure proper orchestration and prompt design
- Ensure security, authentication and authorization flows between services (API-to-API, OAuth, SSO)
- Collaborate with other teams to integrate with internal systems and data platforms
- Contribute to CI/CD pipelines (GitHub / Azure DevOps)
- Optimize performance, scalability and cost of AI solutions
- Support the transition from PoC to industrialized production systems
Tech Environment (typical)
- Azure (Azure ML, Azure OpenAI, AKS, Functions, Storage)
- Python (FastAPI, ML/AI frameworks)
- Kubernetes (AKS)
- APIs & microservices architecture
- CI/CD (GitHub / Azure DevOps)
- Security protocols (OAuth2, SSO, managed identities)
- Data pipelines & integration with enterprise systems
Profile
- Strong experience as AI Engineer / ML Engineer / Cloud Engineer (AI-focused)
- Hands-on experience with Azure cloud ecosystem
- Experience deploying AI/ML models into production environments
- Solid understanding of APIs, microservices and distributed systems
- Experience with LLMs / GenAI use cases is a strong plus
- Comfortable working in complex enterprise environments
- Ability to balance hands-on work and stakeholder interaction
