AI Agent Developer

Role Summary We’re hiring an AI Agent Developer to own the technical delivery of an enterprise AI agent / AI-native engineering platform that supports task automation for our Finance Operation business users. You’ll combine architectural design with hands-on, full-stack engineering, writing production code to build secure, scalable services and user experiences that developers and business users will adopt.

You’ll integrate large language models (LLMs), AI agents and knowledge augmentation into the solution design, from requirements and design through coding, testing and environment orchestration. You’ll apply modern full-stack engineering practices: microservices, micro frontends, continuous integration and continuous delivery (CI/CD), integrated development, security and operations (DevSecOps), observability and cloud-native delivery.

Key Responsibilities 1) AI Agent Platform Architecture & Delivery (hands-on) Lead architecture and implementation of an AI agent platform, including task orchestration, tool invocation, context/session management, memory, and multi-agent collaboration.

Translate emerging model capabilities into reusable platform components that teams can adopt safely and quickly, including software development kits (SDKs), services, templates and guardrails that constrain AI behaviour.

Drive real implementation in complex engineering scenarios, not just prototypes—optimised for reliability, latency, and cost. 2) AI-native Engineering Workflow Enablement Embed AI capabilities into Business workflows, covering: AI-assisted agent AI evaluation Natural Language to SQL generation Establish quality and measurement practices for AI features (accuracy, coverage, drift, prompt/tool regression, cost).

3) Knowledge Augmentation (RAG / GraphRAG / Knowledge Graph) Design and implement a knowledge layer that grounds AI responses in enterprise information and improves their correctness, covering: RAG, GraphRAG, and knowledge graph design/integration indexing strategies, retrieval evaluation, and relevance tuning governance for enterprise knowledge usage (permissions, lineage, auditability) 4) Engineering Delivery Excellence (Full Stack + Microservices + DevOps) Stay hands-on delivering production-grade full stack solutions (Python backend microservices/ React frontend UI), applying secure engineering practices and strong system design.

Build and operate cloud-native services with CI/CD, DevSecOps, test automation, environment management, and robust observability (metrics/logs/traces), driving high availability and operational readiness. Collaborate across Product/Engineering/QA/Operations to standardise, productionise and continuously improve platform capabilities; mentor engineers and uplift delivery maturity using practical engineering metr...

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