Capco
Mid Level AI Engineer
About this role
Capco seeks a mid-level AI engineer to design and deploy generative AI solutions using large language models, RAG systems, and agentic architectures. You'll develop intelligent applications that solve complex business challenges while collaborating with cross-functional teams in a hybrid Orlando role.
What you'll do
- Design and deploy AI applications using LLMs, foundation models, and 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 and business systems
- Build scalable REST APIs, microservices, and cloud-native backend architectures
- Translate business requirements into production-ready AI solutions with cross-functional teams
What they're looking for
- Python development
- Large Language Models (OpenAI, Anthropic, Gemini, Azure OpenAI)
- Retrieval-Augmented Generation (RAG)
- Vector databases (Pinecone, Weaviate, Chroma, Qdrant, FAISS)
- REST APIs and microservices
- Cloud platforms (AWS, Azure, Google Cloud)
- Git, CI/CD, and testing
- Multi-agent frameworks (LangChain, LangGraph, CrewAI)
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
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
- Walk us through a production Generative AI application you built. What LLM provider did you use, and what were the key challenges you faced in deployment?
- Describe your experience implementing RAG systems. Which vector database have you used, and how did you optimize retrieval quality for your use case?