Artificial Intelligence Engineer

Technical Development

• Develop platform functionality using Python, building APIs and integrations to extend

capabilities for diverse client needs.

• Design and implement LLM-powered applications and workflows using open source

models such as Llama, Qwen and Gemma, as well as those online models from

OpenAI, Gemini, etc.

• Build AI agents with tool/function calling, prompt engineering and appropriate

guardrails using frameworks such as OpenAI AgentSDK, LangGraph or LlamaIndex.

• Implement testing and evaluation frameworks for LLM applications, covering prompt

testing, output quality metrics and agent behaviour validation.

• Apply relevant AI technologies as needed, including retrieval systems (RAG,

GraphRAG), knowledge graphs, vector databases or data pipelines.

• At least five years as a software engineer on commercial platforms, with

demonstrable experience building production LLM-powered applications.

• Proven experience with API-level LLM usage, including tool/function calling, prompt

engineering and evaluation.

• Experience with agent frameworks (OpenAI AgentSDK, LangGraph, LlamaIndex

Agents or similar).

• Experience developing APIs using FastAPI or similar frameworks and integrating with

third-party platforms.

• Direct client-facing experience gathering requirements and delivering technical

implementations.

• Experience within agile development workflows and engineering teams.

Skills & Abilities

• Strong Python (or similar) programming skills with a focus on production-grade

applications.

• Excellent communication abilities, translating complex technical concepts for diverse

audiences.

• Strong analytical and problem-solving approach, identifying scalable and reusable

solutions.

• Leadership qualities, including technical mentorship, team collaboration and line

management.

• Ability to align solutions with business goals and industry-specific constraints.

• Self-sufficient contributor capable of working independently and seeking support

when needed.

Nice to Have

The following are examples of specialised areas that would be valuable. Deep expertise in

some of these areas is preferred over surface-level knowledge across all domains.

• Open source LLMs (Llama, Qwen, Gemma, GPT OSS) and local deployment

strategies.

• Frameworks and protocols such as Model Context Protocol (MCP) or Agent-to-Agent

(A2A).

• LLM evaluation tooling (OpenAI Evals, LangSmith, custom evaluation harnesses).

• Advanced agent patterns: multi-agent systems, supervision, delegation strategies.

• RAG, GraphRAG and knowledge graph design and implementation.

• Vector databases and similarity search systems.

• Graph databases (ArangoDB, Neo4j, Neptune) and property graph modelling.

• Data engineering: ETL pipelines, document processing, schema design for AI

applications.

• Cloud platforms (GCP preferred, AWS/Azure also relevant) and containerisation

(Docker).