AI Solutions Engineer

Job description

“I am hugely excited about my future and the future of CyberOne. I have enjoyed my time here immensely and have learnt a huge amount in a short space of time, year-for-year I've learnt more here than I have at Microsoft and PwC.” - CyberOne Consultant About CyberOne CyberOne is a pure-play Microsoft security partner dedicated to helping enterprises realise the full value of the Microsoft Security portfolio—across Defender XDR, Sentinel, Entra, Purview, Intune, Copilot for Security and more. We combine deep technical expertise with outcome-driven services that accelerate secure cloud adoption, modernise threat protection and simplify compliance. Job Title: AI Solutions Engineer Location: Fully Remote/PH Employment Type: Full-time | Contractual Reports to: Microsoft Practice Director CyberOne are seeking an experienced AI Solutions Engineer to help our organisation identify, design and deliver practical AI and automation solutions across the business to improve internal efficiency. This is a greenfield opportunity where you will be responsible for identifying, designing and implementing AI-driven solutions that improve the way the business operates. The Role This role sits at the intersection of AI engineering, solution design, business analysis and transformation. The successful candidate will work closely with stakeholders across the organisation to understand how different teams operate, identify opportunities for improvement, and design practical AI solutions that deliver measurable business value. Rather than simply building AI tools, you will be expected to understand existing business processes, challenge current ways of working and help define future-state processes that leverage AI, automation and intelligent agents. What You'll Be Doing You will work across different areas of the organisation to understand how work gets done today and identify opportunities where AI, agents, and automation could improve it. Your work will involve: Running discovery sessions with business and technical teams Mapping existing processes, systems, pain points and manual activities Identifying and prioritising opportunities for AI and automation Challenging existing processes and designing improved future-state workflows Translating business problems into technical requirements and solution designs Building prototypes and proofs of concept to validate ideas quickly Developing AI applications, agents and automation workflows Integrating AI solutions with existing systems, APIs and data sources Integrating disparate systems to automate workflows and remove manual effort Designing appropriate human-in-the-loop controls, guardrails and monitoring Testing solutions and measuring their effectiveness against defined business outcomes Taking successful prototypes through to production Supporting teams with adoption and new ways of working Helping establish reusable AI architecture, engineering patterns and best practices Contributing to the organisation's longer-term internal AI and automation roadmap The Types of Solutions You Might Build Depending on the opportunities identified, solutions could include: AI systems, agents, or automation that perform multi-step business processes Automation of current manual processes Connecting up distinct systems to improve data flows Customer or employee support assistants Workflow automation combining AI with existing business systems AI and automation capabilities embedded into existing applications What We're Looking For: You don't need experience with every technology or use case, but you should have strong experience building real-world software and applying modern AI and automation solutions to practical problems in order to solve business challenges. Essential Strong software engineering experience (use of languages is flexible) Hands-on experience building applications using a variety of AI platforms Experience building AI agents, tool-calling workflows or deterministic automation Experience integrating applications with APIs, databases and external systems Ability to understand and analyse business processes Strong solution design and architecture skills Experience translating ambiguous business problems into practical technical solutions Ability to prototype rapidly and iterate based on user feedback Strong stakeholder communication and consulting skills Comfortable working independently in a greenfield environment Useful Experience: Experience with some of the following would be valuable: Agent and workflow orchestration frameworks RAG, embeddings, vector search and enterprise knowledge retrieval Evaluation and monitoring of LLM applications Prompt and context engineering Cloud platforms such as Azure, AWS or GCP Workflow and automation platforms Enterprise system integrations AI security, permissions, governance and guardrails CI/CD and production deployment of AI applications What Success Looks Like: Success in this role isn't measured by the number of AI prototypes created. It is measured by whether those solutions improve how the organisation operates. Examples could include: Reducing manual processing time Automating repetitive tasks / work Improving response or turnaround times Increasing operational capacity / efficiency Reducing errors or unnecessary hand-offs Improving access to organisational knowledge Enabling employees to make faster, better-informed decisions Moving successful AI ideas from prototype into production You will probably enjoy this role if you like taking problems that initially sound like "we think AI could help here" and turning them into "here is the redesigned process, here is the working solution, and here is the measurable impact it has delivered." Working Style This role requires someone who can operate comfortably across both business and technology. You should be able to run a discovery session with senior stakeholders, understand how an operational team actually works, challenge assumptions constructively, design an appropriate technical solution and then get hands-on to help build it. Because this is a greenfield environment, you will need to be comfortable with ambiguity, take ownership and help establish the patterns and foundations that future AI initiatives can build upon.