About the role

Senior AI Engineer building AI-powered capabilities into Noibu's ecommerce analytics platform. Focused on production AI systems, workflows, and team collaborations.

Responsibilities

Design and build production AI systems — architect autonomous agents, multi-agent orchestration workflows, and retrieval-augmented generation (RAG) pipelines that ship to real users
Build agentic workflows with LangGraph — implement stateful, multi-step agent workflows including tool use, error recovery, human-in-the-loop patterns, and conditional branching
Integrate LLMs into the product — connect to models from OpenAI, Anthropic, Gemini and open-source providers using clean integration patterns (stable interfaces, structured outputs, and function/tool calling)
Own production reliability — manage prompt versioning, trace logging (LangSmith), evaluation pipelines, regression testing, and observability for all AI systems
Shape AI strategy — evaluate emerging models, frameworks, and techniques; make pragmatic build-vs-buy decisions; define and track AI performance metrics (latency, cost-per-query, retrieval accuracy, agent success rates)
Collaborate cross-functionally — partner with Product, Design, and customer-facing teams to identify high-impact AI use cases that solve real customer pain points
Mentor and lead — raise the AI literacy of the engineering org through code reviews, tech talks, documentation, and hands-on guidance

5+ years of professional software engineering experience, with at least 2 years focused on AI/ML or LLM application development
Hands-on production experience with LangGraph, or comparable agentic AI frameworks
Strong Python proficiency with clean, testable code practices
Experience building and deploying RAG systems, including vector databases, embedding models, retrieval strategies, and re-ranking
Deep understanding of LLM APIs (OpenAI, Anthropic, Gemini, open-source), including function calling, structured outputs, streaming, prompt/context engineering, and token management
Experience putting AI systems into production with proper monitoring, evaluation, and observability (e.g., LangSmith, custom eval pipelines)
Strong software engineering fundamentals: version control, CI/CD, testing, code review, and system design
Knowledge of prompt engineering best practices, including chain-of-thought, few-shot, and structured output techniques
Experience with multi-agent systems and orchestration patterns (supervisor, hierarchical, or collaborative agent architectures)