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 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 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.

Required Experience

• •Commercial experience developing AI/LLM applications .
• •Hands-on experience with LangGraph and agentic workflow development.
• •Strong Python development experience.
• •Experience deploying AI applications into production.
• •Strong understanding of LLMs, RAG, tool calling, structured outputs, and prompt engineering.
• •Experience integrating AI applications with APIs, databases, enterprise systems, or SaaS platforms.
• •Experience with cloud platforms such as AWS, Azure, or GCP .
• •Experience with AI evaluation, monitoring, and observability.

Desirable Skills

• •LangChain / LangSmith
• •Multi-agent systems
• •AI workflow orchestration
• •Vector databases
• •Kubernetes
• •Docker
• •FastAPI
• •Data pipelines
• •MLOps
• •AI security and governance
• •Enterprise process automation
• •Experience with financial services, healthcare, retail, manufacturing, or other complex enterprise environments

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