Summary:
We are looking for an AI Engineer to help design, build, and improve AI-powered applications and agentic systems. This role is broad and best suited for engineers who have hands-on experience working with LLMs, RAG pipelines, AI agents, and modern AI orchestration frameworks.
General Information:
The engineer will work on building production-ready AI solutions that combine LLMs, tools, retrieval systems, workflows, and integrations. The ideal candidate is comfortable moving from experimentation to implementation and can make practical engineering decisions around reliability, scalability, and maintainability.
Tasks and deliverables:
Design and build AI agents and LLM-powered workflows.
Implement RAG systems using vector databases, embeddings, chunking strategies, and retrieval optimization.
Work with frameworks such as LangChain, LangGraph, LlamaIndex, or similar tools.
Integrate LLMs with APIs, internal systems, databases, and external services.
Evaluate model outputs, improve prompt strategies, and support testing and observability.
Collaborate with product and engineering teams to turn AI use cases into reliable software.
Required Experience:
Strong software engineering experience, preferably with Python and modern backend systems.
Hands-on experience building AI agents, RAG pipelines, or LLM-based applications.
Experience with LangGraph, LangChain, LlamaIndex, Semantic Kernel, or similar frameworks.
Understanding of embeddings, vector databases, retrieval strategies, and prompt engineering.
Experience integrating LLMs with tools, APIs, and structured workflows.
Ability to evaluate AI system quality, latency, cost, and reliability.
Comfortable working in fast-moving, ambiguous environments.
Engagement highlights:
Broad AI engineering role focused on practical, production-oriented solutions.
Opportunity to work with modern agentic architectures and LLM tooling.
Ideal for engineers who enjoy combining software engineering, experimentation, and AI system design.