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 across the Growth AI Performance System frontend: React and TypeScript component development, API integration, and the full feature lifecycle from design handoff through to production deployment and ongoing maintenance
• Own specific feature areas and interface workstreams across the suite: channel allocation dashboards, scenario planning and what-if tools, marketing ROI output views, brand analytics interfaces, and pricing strategy displays, designed for expert users who need to navigate dense analytical content quickly
• Build data-dense, chart-heavy interfaces using modern visualization libraries; implement time series views, waterfall charts, allocation comparisons, multi-variable scenario tools, and real-time output displays that update as model inputs change
• Implement human-agent interaction components in collaboration with the AI Engineering team: how agentic recommendations, confidence indicators, scenario outputs, and uncertainty are surfaced to users in interfaces that are clear, credible, and professionally appropriate
• Build and maintain the backend API layer that serves domain model outputs, agentic workflow results, and platform data to the frontend; design APIs that are reliable, versioned, and performant at production scale
• Lead the engineering implementation of the Growth AI Performance System design system: build component libraries that faithfully implement design specifications, maintain them as the design system evolves, and establish engineering conventions that make the frontend codebase consistent and maintainable across the full suite
• 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
• Own performance and reliability for the interface layer: monitor frontend performance, API latency, and error rates; diagnose and resolve issues that affect the user experience in live client deployments
• Apply DevSecOps principles across the full stack: authentication and authorization, secure data handling for client commercial data, and compliance with privacy obligations relevant to the platform’s enterprise client context
• Mentor engineers on the team, including the Data Engineer; set engineering standards, provide structured code review feedback, and create opportunities for team members to take on progressively more ownership
• Manage a small team of mid- to senior-level engineers day-to-day: run weekly check-ins, unblock work, and serve as the point of escalation for technical and delivery issues
• Own the full performance cycle for direct reports: write and deliver mid-year and annual reviews, set goals, provide constructive feedback, and build individual professional development plans
• Build AI/ML fluency across the team: set expectations for AI-assisted development practices, identify skill gaps, and guide reports’ growth into AI-augmented engineering roles
• Lead recruiting for the team: define hiring needs, participate in interviews, and own onboarding so new engineers reach full productivity quickly
• Create and foster a high job-satisfaction environment that supports continued growth and advancement for the team, while managing conflict and cross-team dependencies to keep delivery on track
• Engage with consulting teams during and after live client deployments to understand where the interface layer is creating friction, performance problems, or credibility issues; translate that feedback into engineering improvements and prioritize them alongside the product roadmap
• Present interface architecture, engineering tradeoffs, and technical decisions to senior stakeholders in clear, non-technical terms; contribute to stakeholder sessions where the platform’s interfaces are demonstrated and explained
• Communicate clearly across design, AI engineering, and product disciplines; translate design specifications into engineering requirements, surface integration constraints to the design team early, and keep product management informed of delivery status and technical risks
• Drive innovation in interface engineering: stay current with emerging frontend technologies, AI-powered interface patterns, and data visualization approaches; bring relevant learnings back to the team and propose improvements to the engineering approach

• Deep proficiency in React and TypeScript for complex frontend development; strong command of component architecture, state management, performance optimization, and the engineering conventions that make large frontend codebases maintainable
• Strong server-side development proficiency; experience building reliable, versioned, performant APIs and backend services that serve analytical and AI-generated outputs to frontend clients at production scale
• Experience building data-dense analytical interfaces: chart-heavy dashboards, real-time scenario tools, multi-variable comparison views, and allocation displays using modern visualization libraries
• Experience integrating LLM and agentic AI capabilities into user-facing product features; understanding of how to surface AI-generated outputs, streaming responses, and confidence indicators in interfaces that professional users depend on
• Strong engineering fundamentals: clean, testable code; component library development and governance; CI/CD pipeline ownership; automated testing; and production-grade debugging and performance profiling
• Experience deploying and operating full-stack applications on Azure or AWS; proficiency with containerization and understanding of how frontend and backend services are deployed and monitored in a cloud-native environment
• Solid understanding of authentication, authorization, and secure data handling; experience applying DevSecOps principles across a full-stack application in an enterprise context
• Familiarity with design system implementation: able to translate design token specifications, component documentation, and interaction guidelines into engineering conventions that a team can build from consistently
• Active use of AI-assisted coding tools (Cursor, Claude Code, or equivalent) to accelerate development, prototype faster, and raise the engineering productivity floor of the team
• 10 or more years in full-stack software engineering roles with significant hands-on building responsibilities across both frontend and backend
• Direct experience managing a team of engineers: running performance reviews, professional development planning, and day-to-day people management, in addition to technical leadership
• Track record of building and shipping production applications with data-dense analytical interfaces that real professional users depend on
• Experience leading the engineering implementation of a design system or component library across a multi-feature product
• Experience mentoring engineers and sett

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