Role overview
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.
What you'll work on
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