About the Team
The team will be responsible for two things: building AI-powered solutions that make the client’s internal processes more efficient and exploring new AI capabilities that push the boundaries of what our products can do. The team operates in two tracks – one focused on AI enablement with measurable business impact, the other Ai experiments for short, time-boxed experiments and prototypes.
About the Role
You’ll turn ideas into things people can actually try. The Innovation Lab explores new AI capabilities and builds AI-powered solutions and both need working software that people can interact with. Your job is to take the team’s research outputs, experiment results, and solution concepts and build them into functional prototypes, demos, and interfaces.
Speed matters more than polish. You’ll regularly go from a rough idea to a working demo in days, not weeks. You’ll iterate rapidly based on user feedback, build just enough to test an idea properly, and be comfortable throwing work away when an experiment doesn’t pan out. You’ll also support the AI Enablement track when production solutions need frontend work, integration layers, or user-facing interfaces.
As the team matures, some of what you prototype will graduate into production tools internal platforms, self-serve dashboards, real-time monitoring interfaces, and workflow applications. This role will grow from pure prototyping into building and maintaining production-grade internal tooling that the team and its stakeholders rely on daily.
- Build rapid prototypes and proof-of-concepts that turn experiment results and AI research into interactive, testable demos that stakeholders and users can try
- Create frontend interfaces for production AI solutions review dashboards, configuration screens, monitoring views, approval workflows
- Build integration layers between AI systems and existing infrastructure APIs, data connectors, authentication flows, third-party service integrations
- Develop small-scale testing environments where new features can be validated with real users before committing to full development
- Instrument prototypes to capture usage data and feedback support user validation experiments with measurable data
- Work closely with AI/ML engineers to wrap agent workflows in usable, intuitive interfaces
- Support the Data Scientist and Quantitative Researcher by building data collection, visualization, and reporting tools when needed
- Build self-serve tools for internal stakeholders dashboards for tracking solution performance, configuration interfaces for managing agent behavior, and reporting tools that reduce dependency on the data team
- Evolve successful prototypes into production-grade internal applications handle the scale, reliability, and maintainability concerns that come with tools people depend on daily
- Build real-time monitoring and observability interfaces that give the team and stakeholders visibility into how AI solutions are performing in production
- 6+ years of experience as a full-stack engineer building web applications you’re comfortable owning a feature from database to UI
- Strong frontend skills in React – you can build clean, functional interfaces quickly without needing a designer for every screen.
- Strong backend fundamentals with NodeJS as the primary server-side runtime. You can stand up a working backend in a day when a prototype needs one. APIs, databases, and authentication are second nature.
- Working knowledge of Python and . NET – the team’s AI and data work is heavily Python-based, and some existing solutions use . NET. You need to be able to read, contribute to, and integrate with code in both languages.
- Experience integrating third-party services and APIs you’ve connected systems together and dealt with the messiness that comes with real-world integrations
- Comfort with ambiguity and speed you can take a rough idea and figure out the fastest path to a working demo without waiting for a detailed spec
- Willingness to build fast and throw things away you understand that most prototypes are learning tools, not production features, and you’re okay with that
- Enough familiarity with AI/ML concepts to work effectively alongside AI engineers and researchers without needing everything translated for you
- Experience with agile methodologies (Scrum, Kanban, or similar)
- Experience building interfaces for AI-powered products chat UIs, content generation tools, review or approval workflows, dashboards for AI system monitoring
- Deep experience with Python or . NET beyond the baseline – e.g., building your own services or pipelines in these languages, not just integrating with them
- Experience building production-grade internal tools or platforms not just prototypes, but applications that teams rely on daily
- Experience with real-time data visualization or monitoring dashboards
- Experience with rapid prototyping tools or frameworks that accelerate demo-building
- Background in user research or usability testing you know what to watch for when someone tries your prototype for the first time
- Experience building self-serve analytics or configuration tools for non-technical users
To apply for this job, please visit the application page
