AI Solutions Engineer – Generative AI & Agentic Automation

Role Overview

Can you turn complex business problems into AI solutions that actually work?

We are looking for a hands-on AI Solutions Engineer who can go beyond building prototypes to design, develop, and deploy production-ready AI solutions.

This is an opportunity to work at the intersection of Generative AI, AI Agents, Cloud Engineering, and Business Process Automation, solving real operational challenges through intelligent technology.

You will work directly with business stakeholders to uncover inefficiencies, identify automation opportunities, design intelligent workflows, and turn ideas into working solutions.

We are looking for someone who can understand the problem, design the solution, write the code, and take ownership of delivery.

Key Responsibilities

Business Process Discovery & Solution Design

• Engage business stakeholders to understand operational challenges, existing workflows, and automation opportunities.

• Conduct requirements-gathering workshops and process discovery sessions.

• Analyse existing business processes and identify opportunities for AI-driven automation and optimisation.

• Translate business requirements into process maps, workflow diagrams, and technical solution designs.

• Document current-state and future-state workflows using BPMN, swim-lane diagrams, value stream mapping, or similar techniques.

• Develop solution blueprints that clearly define business requirements, technical architecture, and expected outcomes.

AI Agent Development & Workflow Automation

• Design and develop AI agents, LLM-powered applications, and intelligent automation workflows.

• Define agent workflows covering triggers, decision logic, tool integration, human approvals, exception handling, and failure recovery.

• Build AI solutions using approved Large Language Models (LLMs) and enterprise AI services.

• Design secure AI workflows that comply with data protection, access control, and restricted-environment requirements.

• Evaluate when AI-driven automation is appropriate and when human oversight or approval is necessary.

• Ensure AI solutions are reliable, maintainable, secure, and suitable for production deployment.

Software Engineering & Technical Implementation

• Develop production-ready applications and automation solutions using Python or other backend programming languages.

• Build, deploy, and maintain solutions on cloud-native infrastructure.

• Develop and manage containerised applications within enterprise environments.

• Integrate applications and systems using REST APIs, event-driven architectures, and workflow automation.

• Implement appropriate logging, monitoring, error handling, and recovery mechanisms.

• Apply DevSecOps practices including Git, CI/CD pipelines, automated deployments, and Infrastructure as Code.

• Configure and troubleshoot cloud resources including compute, networking, and Identity and Access Management (IAM).

• Develop clean, reusable, well-documented code that can be maintained and extended by other engineering teams.

• Support application deployment within secure, restricted, or air-gapped environments where required.

• Troubleshoot technical issues and optimise application performance, reliability, and scalability.

Stakeholder Engagement & Delivery

• Take ownership of assigned AI solutions from discovery and design through development, deployment, and handover.

• Collaborate with business users, solution architects, platform engineers, infrastructure teams, and cybersecurity specialists.

• Present technical designs, solution options, risks, and implementation approaches to stakeholders.

• Explain complex technical concepts clearly to both technical and non-technical audiences.

• Prepare technical specifications, solution documentation, operational guides, and knowledge-transfer materials.

• Ensure delivered solutions meet business objectives, technical requirements, and security standards.

Required Skills

• Degree in Computer Science, Information Technology, Software Engineering, Engineering, or a related discipline.

• Strong programming skills in Python or another backend programming language, with experience developing production-ready applications.

• Practical understanding of Generative AI, Large Language Models (LLMs), AI agents, and AI-driven workflow automation.

• Ability to design and implement AI workflows involving decision logic, API integrations, tool usage, and human-in-the-loop controls.

• Experience working with cloud infrastructure, including compute, networking, IAM, and application deployment.

• Knowledge of containerisation and orchestration technologies used in enterprise environments.

• Experience with REST APIs, system integrations, and workflow automation.

• Understanding of DevSecOps practices including Git, CI/CD pipelines, and Infrastructure as Code.

• Ability to independently gather requirements and translate business challenges into technical solutions.

• Experience documenting business processes using BPMN, workflow diagrams, process maps, or equivalent methodologies.

• Strong understanding of software engineering principles, application security, and production deployment practices.

• Ability to troubleshoot technical issues and deliver maintainable, reliable solutions.

• Strong analytical, problem-solving, communication, and stakeholder management skills.

• Ability to work independently, manage ambiguity, and take ownership of solutions from concept through implementation.

• Strong awareness of security requirements when working with sensitive or restricted information.

Preferred Skills

• Experience developing enterprise AI agents, GenAI applications, or intelligent automation solutions.

• Familiarity with AI agent orchestration frameworks and LLM application development tools.

• Experience with Kubernetes or other container orchestration platforms.

• Knowledge of AWS, Microsoft Azure, or Google Cloud Platform.

• Experience working in government, defence, public sector, or highly regulated enterprise environments.

• Exposure to classified, restricted-data, or air-gapped technology environments.

• Understanding of AI safety, governance, and responsible AI implementation.

• Familiarity with IT Service Management (ITSM) platforms and ITIL processes.

• Experience supporting business process transformation and enterprise automation initiatives.

Application Note

Ready to build AI solutions that move beyond experimentation and deliver real business impact?

We are particularly interested in engineers who can demonstrate hands-on experience designing, developing, and deploying working AI or automation solutions.

Interested applicants may send their CV directly to [email protected] for consideration.

Candidates are encouraged to highlight relevant AI, Python, cloud, or automation projects they have personally designed and implemented.