AI Solutions Engineer; Senior

Position: AI Solutions Engineer (Senior)

Whitespace Belfast, Northern Ireland, United Kingdom

AI Solutions Engineer (Senior)

Whitespace Belfast, Northern Ireland, United Kingdom

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Reporting to: Head of Solutions Engineering

About Whitespace

Whitespace leads in creating smarter, safer AI solutions for enterprises. As AI presents transformative opportunities, many companies struggle to harness its full potential.

Whitespace bridges this gap with Collective - The AI Operating System for high-assurance environments. Built for scale, trust and action - even in the most secure, complex or disconnected settings.

We're not just building AI systems. We're laying the foundation for a future where humans and machines work together like never before.

Role Summary

The successful AI Solutions Engineer will exploit, extend and enhance our AI Operating System, which leverages LLMs to solve industry-specific challenges across regulated industries, including defence, legal, finance and others.

This is a hands‑on 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 the latest frameworks and technologies.

Responsibilities 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 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.

Solution Design and Implementation

• Design and deliver scalable, efficient solutions tailored to client‑specific requirements whilst balancing innovation with practical constraints around compliance, performance, cost and security.
• Participate in technical discussions, diagnose complex challenges and collaborate effectively within the engineering team.

Client Engagement and Collaboration

• Work directly with clients to gather requirements and translate them into actionable AI‑powered solutions, moving rapidly from concept to working prototype.
• Communicate complex technical concepts effectively to technical and non‑technical stakeholders, including system capabilities and limitations.
• Collaborate with team members and contribute to technical knowledge sharing within the team.

Role Requirements Work Experience

• At least three years as a software engineer on commercial platforms, with demonstrable experience building LLM‑powered applications.
• Proven experience with API‑level LLM usage, including prompt engineering and tool/function calling.
• 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 scalable and secure solutions.
• Excellent communication abilities, translating complex technical concepts for diverse audiences.
• Strong analytical and problem‑solving approach, identifying scalable and reusable solutions.
• Ability to align solutions with business goals and industry‑specific constraints.
• Self‑sufficient contributor capable of working independently and seeking support when needed.
• Collaborative team player who works effectively with colleagues across the organisation.

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).
• L…