AI Architect (with Python)

Spyrosoft is an authentic, cutting-edge software engineering company, established in 2016. It was among the fastest growing technology companies in Europe in 2021 and 2022, according to the Financial Times. The company specializes in technology solutions for industry 4.0, automotive, geospatial, healthcare & life sciences, employee experience & education, and financial services industries.

• 7+ years of software engineering experience
• 3+ years of commercial experience delivering AI or Generative AI solutions
• Expert-level Python programming skills
• Strong experience with LLM-based applications
• Experience with frameworks such as LangChain, LangGraph, LlamaIndex, or similar
• Experience designing RAG architectures
• Experience with vector databases (Pinecone, Weaviate, Milvus, Chroma, Azure AI Search, or similar)
• Experience integrating AI services through APIs
• Knowledge of prompt engineering techniques
• Experience deploying AI solutions to cloud environments (Azure, AWS, or GCP)
• Familiarity with containerization (Docker) and Kubernetes
• Experience with CI/CD and software architecture best practices
• Excellent communication and stakeholder management skills
• Fluent English (C1)

Nice to have:

• Experience with AI Agents and multi-agent systems
• Experience with MLOps or LLMOps
• Knowledge of Azure AI Foundry, Azure OpenAI, AWS Bedrock or Google Vertex AI
• Experience with evaluation and monitoring of LLM applications
• Relevant cloud or AI certifications

• Design end-to-end AI solution architectures for enterprise environments
• Lead the technical design of Generative AI and Machine Learning solutions
• Build production-ready AI services using Python
• Define AI platform architecture, integration patterns and deployment strategies
• Design scalable RAG (Retrieval-Augmented Generation) solutions
• Select appropriate LLMs, vector databases, and AI frameworks
• Collaborate with Software Engineers, Data Engineers, Data Scientists, and business stakeholders
• Define security, governance, and responsible AI best practices
• Review solution designs and mentor engineering teams
• Drive AI engineering standards and best practices across projects