AI Engineer

Base pay range

Direct message the job poster from Few&Far

The successful AI Solutions Engineer will extend and enhance our AI Operating System, which leverages LLMs to solve industry-specific challenges across defence, legal, health, infrastructure and management consulting sectors.

This is a hands‑on lead role focused on rapidly prototyping and deploying AI‑powered solutions. Working directly with clients, you will translate their needs into scalable, production‑ready AI applications using modern frameworks and techniques.

• 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 online models from …
• Build AI agents with tool/function calling, prompt engineering and appropriate guardrails using frameworks such as OpenAI Agent
  
  SDK, Lang Graph or Llama Index.
• 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, Graph
  
  RAG), knowledge graphs, vector databases or data pipelines.
• Role Requirements
• 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 Agent
  
  SDK, Lang Graph, Llama Index 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 with in 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
• 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, Lang Smith, custom evaluation harnesses).
• Advanced agent patterns: multi‑agent systems, supervision, delegation strategies.
• RAG, Graph
  
  RAG and knowledge graph design and implementation.
• Vector databases and similarity search systems.
• Graph databases (Arango
  
  DB, Neo4j, Neptune) and property graph modelling.
• Cloud platforms (GCP preferred, AWS/Azure also relevant) and containerisation (Docker).
• Observability and monitoring for LLM applications (tracing, metrics, cost tracking).
• Secure coding practices for regulated industries and sensitive data handling.

Mid‑Senior level

Contract

Information Technology

Information Services and Defense and Space Manufacturing