Full stack AI engineer

You will be part of a team building Bain’s integrated suite of agentic decisioning tools that gives large organizations always-on intelligence across their most important growth decisions. The role sits at an intersection few organizations can offer: the analytical depth and client access of a top-tier consultancy, the technical ambition of a product company, and engineering work at the frontier of applied AI.

• Build and ship end-to-end product features within a Growth AI Performance System module, from design handoff and API integration through to production deployment and maintenance, owning specific interface areas responsible for both the backend service layer and the React components that surface capabilities to users
• Implement the core analytical interfaces of your module: marketing ROI dashboards and channel allocation views, brand equity outputs and scenario comparisons, or pricing elasticity and promotional lift tools, designed for expert users who need to navigate dense analytical content quickly and confidently
• Build scenario planning and what-if interfaces where users adjust model inputs and see outputs update in real time; implement the data-binding, state management, and API design that makes these interactions fast and reliable under the latency constraints of a live client session
• Integrate Data Scientist model outputs and AI Engineering agentic capabilities into user-facing product features; build the API and data transformation layer that structures, validates, and serves analytical outputs to the frontend in a form that interfaces can consume reliably
• Implement data visualization components for module-specific analytical content: time series views, waterfall charts, allocation comparisons, elasticity curves, and scenario output displays, using modern visualization libraries with the performance and fidelity that professional-grade analytical tools require
• Write clean, testable, maintainable code across the full software development lifecycle; implement CI/CD pipelines, automated testing, and deployment workflows that support rapid iteration without sacrificing production stability in live client environments
• Collaborate closely with the Data Scientist on how model outputs are structured for product consumption: define data schemas, API contracts, and output formats that give the interface layer what it needs without imposing constraints that limit modeling choices
• Work from design specifications produced by the Design Lead; implement components faithfully, flag feasibility constraints early, and contribute to the feedback loop between design and engineering that keeps the interface quality high across the module
• Contribute to the module’s data pipeline and API layer in collaboration with the Data Engineer: ensuring that the data flowing from upstream sources through transformation logic to the frontend API is validated, consistently structured, and served with the latency the interface requires
• Apply DevSecOps principles across the full stack: authentication and authorization, secure handling of client commercial data, and compliance with the privacy obligations relevant to enterprise client deployments
• Engage with consulting teams during and after live client deployments to understand where the product interface is creating friction, performance problems, or credibility issues; translate that feedback into prioritized engineering improvements
• Contribute to senior stakeholder sessions where module interfaces are demonstrated and explained; develop the ability to present engineering decisions and interface tradeoffs in business terms to consulting teams and, over time, to senior stakeholders
• Communicate clearly across the module team: with the Tech Lead on architecture decisions, with the Data Scientist on model output integration, with the Design Lead on implementation of design specifications, and with the Data Engineer on the data access layer
• Drive continuous improvement: proactively identify opportunities to make the product faster, more reliable, or more useful for the professional users who depend on it; bring that instinct to every sprint without waiting to be asked

• Strong proficiency in React and TypeScript for complex frontend development; solid command of component architecture, state management, and the engineering conventions that make product codebases maintainable as features accumulate
• Strong server-side development proficiency; experience building reliable, versioned APIs and backend services that serve analytical and AI-generated outputs to frontend clients at the latency and reliability standards live client deployments require
• Experience building data-dense analytical interfaces: chart-heavy dashboards, real-time scenario and what-if tools, multi-variable comparison views, and allocation displays using modern visualization libraries
• Experience integrating statistical model outputs and AI-generated content into user-facing product features; understanding of how to structure, validate, and serve analytical outputs in a form that frontend interfaces can consume reliably
• Solid engineering fundamentals: clean, testable code; CI/CD pipeline ownership; automated testing; production-grade debugging and performance profiling; and the discipline to build features that are maintainable by the full team
• Experience deploying and operating full-stack applications on Azure or AWS; familiarity with containerization and how frontend and backend services are deployed and monitored in a cloud-native environment
• Understanding of authentication, authorization, and secure data handling; experience applying DevSecOps principles across a full-stack application serving sensitive client commercial data
• Active use of AI-assisted coding tools (Cursor, Claude Code, or equivalent) to accelerate development, prototype faster, and raise the engineering quality floor across the module team
• 5 or more years in full-stack software engineering roles with hands-on building responsibilities across both frontend and backend
• Track record of shipping production applications with data-dense analytical interfaces that real professional users depend on
• Experience integrating data science or ML model outputs into product features; understanding of how statistical model results need to be structured and served for reliable frontend consumption
• Experience working in agile, cross-functional product teams alongside data scientists and AI engineers; strong troubleshooting and production issue resolution skills
• Associate’s degree / bachelor’s degree in computer science, engineering, mathematics, or a related technical field; or an equivalent combination of education, training, and experience.
• Будет плюсом: Familiarity with domain-specific data patterns in at least one product module area (media spend and attribution data, brand tracking and panel data, or pricing and promotional data) is a strong advantage; Experience in analytics, commercial software, or consulting-adjacent product environments is a strong advantage; Experience in client-facing or consulting-adjacent environments where interface decisions have direct consequences for how professional users trust and act on analytical outputs is a strong advantage

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