Mid Level AI Engineer

About the role

Mid Level AI Engineer

Location: Orlando, FL, USA (Hybrid) | Practice Area: Technology & Engineering | Type: Permanent

Build the next generation of AI-powered solutions using Large Language Models, agentic systems, and cutting-edge Generative AI technologies.

The Role

Capco is seeking a Generative AI Engineer to help design, develop, and deploy intelligent applications powered by Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agent-based architectures. You will work alongside software engineers, product managers, and AI specialists to deliver scalable, production-grade AI solutions that solve complex business challenges and create exceptional user experiences.

This is an exciting opportunity to join a fast-growing AI capability, working on innovative solutions that transform how organizations leverage Generative AI technologies.

What You’ll Do

- Design, build, and deploy AI-powered applications using Large Language Models, foundation models, and modern Generative AI frameworks.

- Develop and optimize Retrieval-Augmented Generation (RAG) solutions leveraging vector databases and enterprise knowledge sources.

- Create intelligent AI agents and workflows capable of interacting with APIs, external tools, and business systems.

- Build scalable REST APIs, backend services, and cloud-native architectures to support AI-powered products.

- Collaborate with cross-functional teams to translate business requirements into robust, production-ready AI solutions.

What We’re Looking For

- 2–3 years of professional software; AI engineering experience.

- Hands-on experience building applications using Generative AI technologies, Large Language Models, and leading LLM providers such as OpenAI, Anthropic, Gemini, Azure OpenAI, or open-source models.

- Strong Python development skills with experience building REST APIs, microservices, and scalable backend applications.

- Practical experience implementing RAG architectures using vector databases such as Pinecone, Weaviate, Chroma, Qdrant, or FAISS.

- Understanding of software engineering best practices including Git, CI/CD, testing, code reviews, and cloud platforms such as AWS, Azure, or Google Cloud.

Bonus Points For

- Experience building multi-agent AI systems using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar technologies.

- Familiarity with Model Context Protocol (MCP) and emerging agent interoperability standards.

- Experience with LLM observability and evaluation platforms such as LangSmith, Langfuse, Arize, or similar tools.

- Experience deploying containerized applications using Docker and Kubernetes.

- Exposure to frontend technologies such as React, TypeScript, modern web frameworks, or contributions to open-source AI projects.