Junior / Middle AI Engineer (Agentic AI / LLM)

AI Engineer (Agentic AI / LLM)

Work Mode: Remote – Europe Working Hours: Must be available for US–Europe time zone overlap, with availability until approximately 18:00–19:00 CET.

About the Role

We are looking for two AI Engineers to join our team and contribute to the development and deployment of AgentQ workflows.

In this role, you will work closely with Senior Agentic AI Engineers and backend engineering teams to build, integrate, test, and optimize AI-powered agent workflows. You will develop agent tools, API integrations, prompt pipelines, retrieval mechanisms, and automated evaluation frameworks, helping transform AI capabilities into reliable, scalable, production-ready solutions.

This is a hands-on engineering role suited to someone with a strong Python background, practical experience building LLM-powered applications, and a genuine interest in agentic AI systems.

Key Responsibilities

• Agent Development: Build, configure, and maintain AI agent workflows, prompt chains, and tool/function-calling integrations within the AgentQ ecosystem.

• API & Backend Integration: Develop REST API connectors, backend services, and lightweight data retrieval pipelines to connect enterprise systems, external APIs, and agent workflows.

• RAG & Data Integration: Implement and optimize Retrieval-Augmented Generation (RAG) pipelines, embeddings, and integrations with vector databases and enterprise data sources.

• Testing & Evaluation: Design and implement automated test suites, benchmark datasets, and evaluation frameworks to measure agent accuracy, response quality, latency, and guardrail compliance.

• Debugging & Optimization: Investigate edge cases, troubleshoot agent behavior, monitor production executions, and continuously improve prompts, retrieval strategies, and fallback mechanisms.

• Documentation: Maintain clear technical documentation covering agent architectures, API interfaces, integrations, testing processes, and operational runbooks.

• Collaboration: Work closely with senior AI engineers and backend teams to deliver reliable AI automation capabilities and support production deployments.

Requirements

• 2+ years of hands-on experience in Python software engineering, backend development, or applied AI/LLM engineering.

• Practical experience building LLM-powered applications, RAG pipelines, or AI agent workflows.

• Familiarity with agentic AI frameworks and technologies such as LangChain, LlamaIndex, and LLM APIs (OpenAI, Anthropic, or Mosaic AI).

• Solid backend engineering fundamentals, including:

• Python and REST API development (FastAPI or Flask).

• JSON schemas and API integrations.

• Asynchronous programming and error handling.

• Git and collaborative software development workflows.

• Understanding of vector databases, embeddings, semantic search, and retrieval techniques.

• Basic experience with SQL and cloud/data platforms such as Databricks, AWS, or Azure.

• Strong analytical and problem-solving skills, with the ability to troubleshoot issues and iterate based on evaluation metrics and user feedback.

• Good communication skills and the ability to collaborate effectively with distributed engineering teams.

• Comfortable working remotely with availability for US–Europe time zone overlap, typically until 18:00–19:00 CET.

Nice to Have

• Experience deploying and maintaining LLM-powered applications in production.

• Familiarity with LLM evaluation frameworks, observability, tracing, and monitoring tools.

• Understanding of AI safety, guardrails, structured outputs, and hallucination mitigation.

• Experience with Docker, CI/CD pipelines, and cloud-based deployment environments.

• Exposure to multi-agent architectures, orchestration patterns, and advanced tool-calling strategies.

What You'll Be Working On

• Building real-world agentic AI workflows rather than standalone AI prototypes.

• Integrating LLMs with enterprise systems, APIs, and structured/unstructured data.

• Improving agent reliability, performance, and scalability through systematic testing and evaluation.

• Collaborating with experienced AI engineers and backend developers.

• Helping bring next-generation AI automation capabilities into production.

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