Business Intelligence - Engineer

What you'll do

1. Design, develop, and deploy AI agent workflows for use cases that require engineering support, including tool integrations, API connections, and RAG pipelines
2. Maintain shared infrastructure that multiple teams can reuse, such as LLM API access, vector databases, and prompt templates
3. Help individual teams build their own AI automations by providing technical direction, code templates, and troubleshooting support
4. Handle complex integrations — connect AI tools to backend systems, internal APIs, and regional platforms that individual teams cannot access independently
5. Optimize for efficiency — manage LLM costs, monitor system performance, and ensure reliability of deployed agents
6. Document what you build so others can maintain, reuse, and build on top of it
7. Support AI adoption — help teams across the company integrate AI into daily workflows and make it easy and accessible
8. Optimize data pipelines and table structures to improve query performance and reduce resource costs
9. Monitor and maintain data infrastructure health — identify inefficiencies, resolve bottlenecks, and ensure reliability for downstream BI reporting

Requirements

• 2+ years of engineering experience in AI/ML systems, backend development, or automation — or fresh graduates with strong fundamentals and genuine hunger to learn
• AI/LLM fundamentals — working knowledge of LLM frameworks (LangChain, LangGraph, or similar), prompt engineering, and RAG systems
• Python proficiency — comfortable building and deploying Python-based tools, APIs, and automation scripts
• API and integration skills — experience connecting systems via APIs, handling data pipelines, working with third-party services, and scraping data from various sources
• Builder mentality — able to ship working prototypes quickly, iterate based on feedback, and take projects from idea to production
• Communication skills — able to explain technical concepts simply to non-technical business stakeholders
• Self-starter — comfortable working with ambiguity, owning initiatives independently, and collaborating across teams
• Strong SQL knowledge — able to write complex queries, optimize table structures, and understand how data flows across pipelines

Nice to Have:

• Experience with observability tools and production monitoring for AI systems
• Familiarity with vector databases (Pinecone, ChromaDB, or similar)