Capco
Mid Level AI Engineer
About this role
Join Capco as a Mid Level AI Engineer to design and deploy production-grade generative AI applications using Large Language Models, RAG systems, and intelligent agents. This hybrid Orlando role focuses on building scalable, business-critical solutions that solve complex enterprise challenges using cutting-edge AI technologies.
What you'll do
- Design, build, and deploy AI-powered applications using LLMs and modern Generative AI frameworks
- Develop and optimize RAG solutions with vector databases and enterprise knowledge sources
- Create intelligent AI agents and workflows that interact with APIs, external tools, and business systems
- Build scalable REST APIs, backend services, and cloud-native architectures for AI products
- Collaborate with cross-functional teams to translate business requirements into production-ready AI solutions
- Implement software engineering best practices including CI/CD, testing, and code reviews
What they're looking for
- Python development (REST APIs, microservices, backend applications)
- Large Language Models and Generative AI (OpenAI, Anthropic, Gemini, Azure OpenAI)
- RAG architecture and vector databases (Pinecone, Weaviate, Chroma, Qdrant, FAISS)
- Multi-agent AI frameworks (LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel)
- Cloud platforms (AWS, Azure, Google Cloud)
- Git, CI/CD pipelines, and containerization (Docker, Kubernetes)
- LLM observability and evaluation tools (LangSmith, Langfuse, Arize)
- Software engineering best practices and code review processes
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Capco
Capco builds AI and generative AI platforms, as well as energy trading and risk management (ETRM/CTRM) solutions for financial institutions and energy organizations. The company is hiring AI DevOps engineers, AI engineers, full-stack developers, and technical specialists to design, deploy, and operate these platforms across cloud infrastructure and trading systems.
- Website
- capco.com
Likely interview questions
- Describe a generative AI application you've built end-to-end—what LLM provider did you use and how did you handle production considerations?
- Walk us through your experience implementing a RAG system. Which vector database did you choose and why?