About the role
You will help shape problems as well as build solutions, working with users whose needs become clearer through experimentation. You can influence technical choices, see how your work performs in practice and help develop Valcon’s reusable AI capabilities and engineering approach. CAREERS WITH VALCON Ready to apply? Case Studies Success stories Prev Next Keeping critical Mendix applications secure, stable and business-ready : Keeping critical Mendix applications secure, stable and business-ready Using a cloud platform to accelerate AI and data product delivery in construction : Using a cloud platform to accelerate AI and data product delivery in construction Faster project delivery and stronger forecasting for a leading energy infrastructure provider : Faster project delivery and stronger forecasting for a leading energy infrastructure provider Improving delivery control for major railway renewal projects : Improving delivery control for major railway renewal projects Creating programme visibility during a large-scale banking separation : Creating programme visibility during a large-scale banking separation Building a product-centric data, analytics and AI operating model in the defence industry : Building a product-centric data, analytics and AI operating model in the defence industry Get in touch Leave us a message and we will get back to you. 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What you'll do
Shadow users and map their activities, hand-offs and exceptions. Identify where AI can reduce repetitive work, improve decisions and give people more time for judgement and creativity. Assess opportunities against value, feasibility, data availability and risk. Explain the case for investment, distinguish quick wins from larger changes and agree how success will be measured. Explore and prepare data, investigate patterns and test hypotheses. Build the datasets and pipelines needed for experiments, working with data engineers and domain specialists where appropriate. Build working prototypes quickly and test them with users. Develop applications (Python preferable) that connect LLMs to relevant context, tools and business systems, choosing agentic or conventional approaches according to the problem. Apply statistical modelling or machine learning where it helps answer the question. Establish baselines, validate results and communicate what the evidence supports, including uncertainty and limitations. Engineer reliable delivery through automated tests, AI evaluations, deployment pipelines and observability. Investigate failures across models, retrieval, tools and integrations, balancing quality with latency and cost. Help clients put solutions into use with appropriate permissions, human review, failure recovery and ongoing ownership. Support changes to workflows, roles and controls, and check whether the expected benefits materialise. Build trust through clear advice and working software. Contribute to proposals and demonstrations, share lessons and develop reusable engineering patterns with the wider team. Consulting and ways of working You listen carefully, lead useful discovery conversations and translate an ambiguous business problem into a practical experiment or delivery plan. You can explain technical choices clearly to users and senior stakeholders. You understand that adoption depends on the surrounding process, skills, controls and ownership. You are willing to contribute to discovery, business cases and change as well as hands-on engineering. We expect working capability across the core areas above, with deeper expertise in at least one. Your strongest background may be software engineering or applied data science. You should be able to explain your contribution to a solution, what failed and how you improved it, and know when to draw on other specialists. Additional strengths Deeper experience in an area such as forecasting, optimisation, computer vision, model adaptation or agent orchestration is welcome. Other useful strengths include MCP integrations, operating production AI systems and using AI coding tools with effective review and testing. These are examples of specialisms you can bring; expertise in every area is not expected.