Développeur backend senior chez GHGSat, fournisseur de données satellitaires et aéroportées sur les émissions de gaz à effet de serre. Conception de services Python, API et solutions AWS évolutives.
About the role
- Enterprise AI Platform Architect at Acquia building and deploying AI workflows in production environments. Leading end-to-end AI agent implementations and optimizing platform scalability and performance.
Responsibilities
- Lead end-to-end delivery of AI agent implementations
- Conduct architectural reviews and provide recommendations for optimizing AI platform performance, scalability, and security
- Lead the design and deployment of production-grade AI systems as an active contributor writing high-impact code. Expect to live in Python, LangGraph, and LangFuse to turn vision into reality.
- Architect sophisticated, stateful, multi-agent workflows using LangGraph. You will build the frameworks that allow autonomous agents to operate with enterprise-level reliability and scale.
- Champion AI observability by integrating LangFuse for deep tracing, prompt versioning, and rigorous evaluation. You’ll turn "black box" LLM interactions into transparent, benchmarked performance data.
- Define the engineering blueprints for the entire organization. You will establish patterns for RAG architecture, advanced tool-calling, context window optimization, and prompt engineering.
- As the internal scout for emerging tech, you will benchmark new LLM providers and orchestration frameworks, ensuring our stack remains at the cutting edge of the agentic revolution.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field
- 7+ years of experience in cloud computing architecture, software engineering, or technical consulting
- 3+ years of experience in AI/ML platform architecture and development, with deep recent experience (2+ years) in generative AI and agentic architectures in production applications.
- Demonstrated track record of shipping AI applications to production environments, not just prototypes.
- Hands-on LangGraph — stateful, cyclic, multi-agent workflows at enterprise scale.
- Hands-on LangFuse - tracing, evaluation, prompt management, and dataset-driven testing
- Python proficiency and strong engineering fundamentals (testing, CI/CD, architecture).
- Cloud AI deployment experience (AWS, Azure, or GCP) including containerization and inference cost management.
- RAG architecture knowledge— vector databases, embedding models, and retrieval strategies.
Benefits
- competitive healthcare coverage
- wellness programs
- take it when you need it time off
- parental leave
- recognition programs
- and much more!
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