AI Application Engineer - LangGraph & Agentic AI

Belmont Lavan Ltd; United States; Remote; USD 120,000 - 170,000; Full time

Job summary

Belmont Lavan Ltd. is seeking an experienced AI Application Engineer to design and build intelligent applications powered by LLMs, LangGraph, and agentic AI technologies.

You will transform business requirements into AI applications that reason through tasks, retrieve information, interact with tools and enterprise systems, request human approvals when needed, and complete business processes.

Qualifications

• Experience turning business requirements into AI applications.
• Ability to design stateful workflows with reasoning and approvals.
• Skill in developing single-agent and multi-agent solutions.
• Knowledge of retrieval augmented generation (RAG) and enterprise data integrations.
• Familiarity with tool calling, prompts, and cost-aware model selection.

Responsibilities

• Design and build AI applications using LangGraph and LLMs.
• Create agents capable of executing multi-step business processes.
• Develop robust, observable AI workflows with retries and fallbacks.
• Connect AI apps to enterprise documents, databases, and APIs.
• Collaborate with product and platform teams to deploy in cloud environments.

Skills

AI integration

Agentic workflows

LLM tooling

RAG architectures

DevOps collaboration

Education

Master’s or PhD in CS/SE

Tools

LangGraph

Vector databases

CI/CD tools

Job description

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 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.

LLM Application Engineering

• 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.

RAG and Enterprise Knowledge

• 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.

Business Process Automation

• 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.

Evaluation and Quality

• 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.

Production Deployment

• 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.

Cross-Functional Collaboration

• Work closely with product managers, business analysts, software