Lead, Full stack AI Engineer

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Who You’ll Work With

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.

You will work inside Bain’s standing product team, building software that helps the world’s largest companies answer the questions that drive their business: where to invest, where to focus, and how to win. The impact of what you build is direct and immediate, and the platform compounds in value with every release.

The platform has two layers working together: an agentic core and a set of decision modules, each solving a distinct commercial problem. The build lifecycle is connected to the real world: as case teams work alongside clients across industries and geographies, that feedback flows straight back to engineering and into the product. Every client engagement makes the platform smarter, broader, and more resilient.

This is a startup inside an established company. You get genuine early-stage ownership and real influence over architecture and product direction, backed by Bain’s global scale, client relationships, and leadership commitment; the pace of a startup with the runway of a global firm.

We are looking for engineers who see how to make a product module better and bring that instinct to every sprint without being asked, and who are comfortable with problems that do not have a known answer. The work involves real-time decision engines, cloud-native infrastructure, and data problems with no off-the-shelf answer. Experience with agentic systems matters: tool and context protocols such as MCP, evaluation harnesses, memory design, and entity resolution across messy enterprise data. This is applied AI in production, at scale, with real users depending on what you build. The field is moving quickly and the team is built to move with it, so the willingness to keep learning counts for as much as what you already know.

Where You’ll Fit Within the Team

The Full-stack Engineer builds and ships the product interfaces and backend services that surface Growth AI Performance System’s analytical capabilities to users within a specific product module, marketing ROI, brand strategy, or pricing and commercial analytics. Working under the Tech Lead and alongside the Data Scientist, you will own specific feature areas within the module: the dashboards, workflow tools, scenario interfaces, and output views that Expert Consultants, specialists, and senior client stakeholders use in live commercial engagements.

You will work across the platform and product modules focused on marketing ROI, brand or pricing strategy, building interfaces that make complex model outputs navigable, credible, and actionable for professional users who need to act on them quickly and present them to senior client leadership. The users of this product are sophisticated; the interfaces need to be fast, dense, and unambiguous.

This role sits at the intersection of product engineering and applied AI delivery. You will integrate the Data Scientist’s model outputs and the AI Engineering team’s agentic capabilities into user-facing features, working from design specifications produced by the Design Lead and contributing to the engineering standards set by the Tech Lead. Your work is the layer between the platform’s analytical intelligence and the client stakeholders who depend on it.

The build lifecycle here is directly connected to real client engagements. Feedback from consulting teams in live deployments flows back into the product, and what you ship in one sprint may be in front of a CMO or Category Director within weeks. You will engage with consulting teams to understand how the product is being used in the field and translate that feedback into engineering improvements.

What You’ll Do

Building and shipping product features

• 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

Engineering quality and collaboration

• 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

Collaboration and communication

• 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

About You

Technical

• 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
• 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
• 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

Experience

• 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 in analytics, commercial software, or consulting-adjacent product environments is a strong advantage
• 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 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
• Experience working in agile, cross-functional product teams alongside data scientists and AI engineers; strong troubleshooting and production issue resolution skills

Education

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.