AI Application Engineer - LangGraph & Agentic AI

Belmont Lavan is seeking an AI Application Engineer to build intelligent applications powered by LLMs, LangGraph, and agentic AI technologies. The role involves designing agentic workflows, integrating enterprise knowledge and systems, automating business processes, evaluating AI applications, and deploying reliable production solutions.

Responsibilities:

• Design and develop AI applications using LangGraph and LLM technologies
• Build agents capable of executing complex, multi-step business processes
• Design stateful workflows incorporating reasoning, tool usage, validation, approvals, and exception handling
• Develop single-agent and multi-agent solutions where appropriate
• Translate business requirements into practical agentic AI architectures
• Integrate LLMs into production applications
• Develop prompt strategies, structured outputs, tool calling, and context-management approaches
• Select appropriate models based on accuracy, capability, latency, security, and cost
• Develop mechanisms to improve reliability and reduce hallucinations
• Implement appropriate guardrails around AI-generated decisions and actions
• Design and implement Retrieval-Augmented Generation (RAG) solutions
• Connect AI applications to enterprise documents, databases, APIs, and knowledge repositories
• Develop retrieval and ranking strategies to provide agents with relevant context
• Work with embeddings and vector databases
• Implement data and context pipelines supporting AI agents
• Analyse business processes and identify opportunities for agentic automation
• Design AI workflows that combine LLM reasoning with deterministic business logic
• Build agents capable of retrieving information, making decisions, invoking tools, and completing actions
• Implement human-in-the-loop approval and escalation processes
• Ensure automated actions are controlled, auditable, and reversible where appropriate
• Develop evaluation frameworks for AI applications and agent workflows
• Define metrics covering accuracy, task completion, reliability, latency, and cost
• Build automated tests for prompts, agents, tools, and end-to-end workflows
• Analyse failures and continuously improve agent behaviour
• Use observability and evaluation data to optimise production systems
• Deploy and operate AI applications in cloud and enterprise environments
• Implement monitoring, logging, tracing, and performance management
• Design resilient workflows with retries, timeouts, fallbacks, and recovery mechanisms
• Work with DevOps and platform teams to establish appropriate deployment and CI/CD practices
• Work closely with product managers, business analysts, software engineers, data scientists, architects, and business stakeholders
• Communicate AI capabilities, limitations, risks, and implementation options
• Help organisations identify realistic and valuable use cases for agentic AI