AI Application Engineer - LangGraph & Agentic AI

We are looking for an experienced AI Application Engineer to design and build intelligent applications powered by LLMs, LangGraph, and modern agentic AI technologies. You will focus on transforming business requirements into AI applications capable of reasoning through tasks, retrieving information, interacting with tools and enterprise systems, requesting human approval when required, and completing business processes. This role sits at the intersection of AI engineering, software development, workflow automation, and business process transformation. Requirements Agentic AI Application Development Design and develop AI applications using LangGraph and LLM technologiesBuild agents capable of executing complex, multi-step business processesDesign stateful workflows incorporating reasoning, tool usage, validation, approvals, and exception handlingDevelop single-agent and multi-agent solutions where appropriateTranslate business requirements into practical agentic AI architectures LLM Application Engineering Integrate LLMs into production applicationsDevelop prompt strategies, structured outputs, tool calling, and context-management approachesSelect appropriate models based on accuracy, capability, latency, security, and costDevelop mechanisms to improve reliability and reduce hallucinationsImplement appropriate guardrails around AI-generated decisions and actions RAG and Enterprise Knowledge Design and implement Retrieval-Augmented Generation (RAG) solutionsConnect AI applications to enterprise documents, databases, APIs, and knowledge repositoriesDevelop retrieval and ranking strategies to provide agents with relevant contextWork with embeddings and vector databasesImplement data and context pipelines supporting AI agents Business Process Automation Analyse business processes and identify opportunities for agentic automationDesign AI workflows that combine LLM reasoning with deterministic business logicBuild agents capable of retrieving information, making decisions, invoking tools, and completing actionsImplement human-in-the-loop approval and escalation processesEnsure automated actions are controlled, auditable, and reversible where appropriate Evaluation and Quality Develop evaluation frameworks for AI applications and agent workflowsDefine metrics covering accuracy, task completion, reliability, latency, and costBuild automated tests for prompts, agents, tools, and end-to-end workflowsAnalyse failures and continuously improve agent behaviourUse observability and evaluation data to optimise production systems Production Deployment Deploy and operate AI applications in cloud and enterprise environmentsImplement monitoring, logging, tracing, and performance managementDesign resilient workflows with retries, timeouts, fallbacks, and recovery mechanismsWork with DevOps and platform teams to establish appropriate deployment and CI/CD practices Cross-Functional Collaboration Work closely with product managers, business analysts, software engineers, data scientists, architects, and business stakeholdersCommunicate AI capabilities, limitations, risks, and implementation optionsHelp organisations identify realistic and valuable use cases for agentic AI Required Experience Commercial experience developing AI/LLM applicationsHands-on experience with LangGraph and agentic workflow developmentStrong Python development experienceExperience deploying AI applications into productionStrong understanding of LLMs, RAG, tool calling, structured outputs, and prompt engineeringExperience integrating AI applications with APIs, databases, enterprise systems, or SaaS platformsExperience with cloud platforms such as AWS, Azure, or GCPExperience with AI evaluation, monitoring, and observability Desirable Skills LangChain / LangSmithMulti-agent systemsAI workflow orchestrationVector databasesKubernetesDockerFastAPIData pipelinesMLOpsAI security and governanceEnterprise process automationExperience with financial services, healthcare, retail, manufacturing, or other complex enterprise environments