AI Solution Development: Design and build AI solutions using generative AI with a focus on automation and process improvement; when required, be able to design, develop, and implement algorithms and models in Machine Learning, Deep Learning, and other AI techniques using Python.
Full lifecycle management of AI applications and tools: Development, deployment, testing/acceptance, release, and maintenance.
Requirements and stakeholder management: Lead meetings with stakeholders to elicit, analyze, and document business requirements, translating them into technical specifications for AI projects.
Communication and presentation: Present results, project progress, and technical insights clearly and persuasively to diverse audiences, from technical teams to executive leadership.
Cloud ecosystem: Use Microsoft Azure services to build, train, and deploy AI solutions at scale.
Quality and best practices: Ensure code quality, model performance, and technical documentation of projects by following software development and MLOps best practices.

Proven experience with AI: Demonstrated experience delivering AI solutions; knowledge of RAG, MCP, and Python frameworks (LangChain, LangGraph, CrewAI, Agno IA).
API experience: REST APIs, SOAP Web Services, FastAPI.
Python experience: Extensive experience developing robust solutions in Python, especially with data
and AI-focused libraries.
Data handling: Proven proficiency across the full data analytics pipeline, from collection and cleaning to visualization (e.g., SQL Server, CosmosDB, etc.), and processing unstructured data (text, audio, and images).
Communication skills: Excellent verbal and written communication skills, with the ability to lead technical discussions and present projects effectively.
Requirements elicitation: Hands-on experience gathering and documenting functional and non-functional requirements with business stakeholders.

AI Engineer, Python

Key skills