AI Application Engineer – LangGraph, Agentic AI

This is a fully remote position, open to applicants in Netherlands, +3 more countries.

📋 Description

• Design and develop intelligent applications utilizing LLMs, LangGraph, and agentic AI technologies.

• Convert business needs into AI applications that can reason through tasks, retrieve information, interact with tools and enterprise systems, seek human approval, and complete business processes.

• Create stateful workflows incorporating reasoning, tool usage, validation, approvals, and exception handling.

• Develop both single-agent and multi-agent solutions.

• Integrate LLMs into production applications using effective prompt strategies, structured outputs, tool calling, and context management.

• Choose models based on criteria such as accuracy, capability, latency, security, and cost.

• Enhance reliability, minimize hallucinations, and establish AI guardrails.

• Design and execute RAG solutions utilizing enterprise documents, databases, APIs, and knowledge repositories.

• Build retrieval, ranking, embedding, vector database, data, and context pipelines.

• Analyze business processes and design agentic automation that merges LLM reasoning with deterministic business logic.

• Implement human-in-the-loop approval and escalation processes.

• Ensure that automated actions are controlled, auditable, and reversible when necessary.

• Develop evaluation frameworks, metrics, automated tests, observability, and continuous enhancements for AI applications and workflows.

• Deploy and manage AI applications in cloud and enterprise settings.

• Implement monitoring, logging, tracing, performance management, retries, timeouts, fallbacks, recovery, deployment, and CI/CD practices.

• Collaborate with product managers, business analysts, software engineers, data scientists, architects, and business stakeholders.

• Communicate the capabilities, limitations, risks, and implementation strategies of AI.

• Assist organizations in identifying practical and valuable agentic AI use cases.

⛳️ Requirements

• Commercial experience in developing AI/LLM applications.

• Hands-on experience with LangGraph and agentic workflow development.

• Strong proficiency in Python development.

• Experience in deploying AI applications in a production environment.

• Solid understanding of LLMs, RAG, tool calling, structured outputs, and prompt engineering.

• Experience integrating AI applications with APIs, databases, enterprise systems, or SaaS platforms.

• Familiarity with cloud platforms such as AWS, Azure, or GCP.

• Experience with AI evaluation, monitoring, and observability.

• Desirable: LangChain / LangSmith.

• Desirable: Multi-agent systems.

• Desirable: AI workflow orchestration.

• Desirable: Vector databases.

• Desirable: Kubernetes.

• Desirable: Docker.

• Desirable: FastAPI.

• Desirable: Data pipelines.

• Desirable: MLOps.

• Desirable: AI security and governance.

• Desirable: Enterprise process automation.

• Desirable: Experience in financial services, healthcare, retail, manufacturing, or other complex enterprise environments.

🏝️ Benefits

• Competitive salary and comprehensive benefits package.

• Opportunities for professional development and career growth.

• Flexible working hours and remote work options.

• A collaborative and innovative work environment.

• Access to cutting-edge technology and tools.