Senior Python AI Engineer (green building)

About the project

Project for a client that promotes green building design.

Our expectations

• Python Expert: 5+ years of commercial experience writing production-quality code. You should be proficient in building scalable services, APIs (FastAPI, Flask, Django), and comprehensive test suites (pytest).

• Clean Code & Architecture: A strong advocate for clean code principles and the ability to build modular, maintainable software architectures.

• Hands-on experience with LangChain or other advanced patterns using LangGraph, LangSmith, or LlamaIndex.

• Agentic Workflows: Proven ability to build autonomous AI agents involving tool-calling, function-calling, and complex multi-step reasoning.

• RAG & Vector Search: Practical expertise in implementing Retrieval-Augmented Generation (RAG) using vector databases (e.g., Pinecone, Weaviate, Milvus, or Pgvector) and optimizing embedding models.

• Prompt Engineering Expert-level skill in prompt design, structured outputs (JSON/Pydantic/Instructor).

• The "Evals" Mindset: You understand that "it works on my machine" isn't enough. You have experience building evaluation frameworks to measure relevance, consistency, latency, cost, and user trust.

• End-to-End Delivery: A track record of taking AI-powered applications from initial ideation and prototyping through to full-scale production deployment.

• Data Literacy: Solid understanding of how data quality, freshness, and structure impact model behavior. Experience with building data pipelines for AI consumption.

• Experience with at least one major cloud platform (Azure, AWS, or GCP), specifically utilizing their managed AI/ML services (e.g., Azure OpenAI, AWS Bedrock).

• Skills in integrating LLMs into existing product ecosystems via microservices or workflow engines.

• High-proficiency in written and spoken English (min. B2+ level).

Welcome Skills

• "Getting Things Done" Attitude: A proactive, problem-solving mindset suited for a fast-paced, client-facing environment.

• Product-First Engineering Mindset: Ownership of the AI lifecycle - from initial ideation and architectural design to production deployment. This includes proactively partnering with stakeholders to refine requirements, identifying the highest-ROI AI patterns, and pivoting quickly based on real-world performance data and user feedback.

• Stakeholder Communication: The ability to translate complex technical AI concepts into clear business solutions for non-technical stakeholders.