Full Stack AI Product Engineer (DataEdge)

Full Stack AI Product Engineer (DataEdge) Job ID 106304 Work Areas: Technology & Engineering Employment Type: Permanent Full-Time Location(s): Atlanta, Austin, Chicago, Dallas, Houston Description & Requirements

WHERE YOU’LL FIT WITHIN THE TEAM

Full-Stack AI Product Engineers build and deliver end-to-end AI product experiences across the PE due diligence platform. This role sits at the intersection of product engineering and applied AI, combining solid full-stack software engineering with practical knowledge of how LLMs and agentic systems behave in production. You will build intelligent product features from backend services through frontend experience, contributing to the workflows, orchestration layers, and user interfaces that make AI useful, reliable, and intuitive for end users. This includes implementing agent workflows, retrieval pipelines, evaluation gates, human-in-the-loop review patterns, and the analyst-facing experiences that surface them. You are technically strong in production AI systems and capable of translating non-deterministic model behavior into clear, trustworthy product experiences with guidance from senior engineers. You will contribute to engineering standards, participate in code reviews, and grow your expertise in building safe, observable, and scalable AI-powered workflows. This role is TypeScript-first. Most of the analyst-facing product surfaces and Node.js services on the Workstream team are built in TypeScript, and we expect this engineer to own and extend that stack end-to-end. Python remains a strong co-requirement for the AI orchestration and backend layer (LangGraph, FastAPI, agent services), so candidates must be comfortable working productively across both ecosystems even where TypeScript is their primary depth.

WHAT YOU'LL DO

Build end-to-end AI product features across backend services, orchestration layers, and frontend user experiences. Develop analyst-facing and internal AI interfaces for workflows such as deal screening, commercial due diligence research, document extraction, and portfolio monitoring. Build responsive, high-quality frontend experiences for streaming AI responses, structured outputs, source grounding, review and approval flows, and human-in-the-loop interactions. Implement full-stack application patterns for chat, copilot, workspace, and review-based AI experiences, including state management, real-time updates, and error handling. Collaborate with Product, Design, and domain stakeholders to translate AI capabilities into intuitive, polished user experiences. Contribute to stable contracts between frontend applications and AI/backend services, ensuring outputs are structured, testable, and resilient. Support contribution workflows and product surfaces for the Prompt Execution Sandbox and AI Artifact Studio, enabling safe and scalable use by non-engineers where required. Ensure AI product features are accessible, observable, and production-ready, with attention to usability, reliability, and edge-case handling. AI Platform and Agent Workflow Engineering (35%) Contribute to the Agent Gateway service, including inbound APIs, model routing, context management, response validation, and cost/audit logging. Build and maintain LangGraph agent workflows for PE use cases, including streaming, tool-calling, multi-step execution, and human-in-the-loop interrupt patterns. Integrate Temporal durable execution with LangGraph, including workflow and activity authoring, checkpointing strategies, retry and backoff policies, and signal/query handling. Contribute to AI platform services such as Agent Session Manager, Memory Service, HITL Coordination Service, and Feedback/Correction Service. Implement RAG pipelines, including chunking strategies, embedding model selection, vector store integration, re-ranking, and retrieval quality evaluation. Support evaluation and regression gates, including golden dataset management, metric definition, qualitative and quantitative evaluation, and CI enforcement on quality regressions. Implement context window management strategies such as token budgeting, truncation/compression, and tool-call state persistence to support reliability in longer-running workflows. Instrument AI services with structured logging, traces, and metrics to support operational dashboards and alerts for latency, quality, cost, and failure signals. Support deployment and operation of AI workloads in Kubernetes, including containerization and Helm-based deployment patterns. Collaboration and Engineering Standards Participate in code reviews and contribute to engineering standards for production AI product engineering across testing, evaluation, documentation, and maintainability. Collaborate with Data Platform on feature store access patterns, inference integration, schemas, and data contracts. Work with Product Engineering and Design on AI feature surfacing, including streaming experiences, structured output rendering, citation and evidence UX, and HITL review interfaces. Use AI coding assistants to accelerate prototyping and development, while validating all production artifacts against testing and evaluation gates before promotion. Document agent behavior specifications, tool contracts, and product interaction patterns so behavior is explicit, reviewable, and maintainable.

ABOUT YOU

Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent practical experience. 3+ years of experience building production software, including experience delivering full-stack applications and/or AI-enabled systems in production environments. Of those years, demonstrable production experience with TypeScript across both frontend and backend (or significant TypeScript backend depth) - not just exposure as a frontend layer on top of a Python service. Python experience is required as a secondary language and can be at a working/contributing level rather than primary depth. Experience contributing to user-facing AI product features, from backend services through frontend implementation. Experience working with agentic systems in production or pre-production, including tool calling, multi-step workflows, RAG, or structured output handling. Exposure to evaluation frameworks, including golden datasets, regression gates, or CI controls for quality assurance. Experience working with containerized environments such as Docker and Kubernetes, including familiarity with monitoring and reliability practices. Experience building modern full-stack applications with frontend architecture and backend integration. Strong TypeScript proficiency as the primary language for full-stack work: Component-based UI development, strict TypeScript (strict mode, generics, discriminated unions), API integration, and application state management. Comfortable owning non-trivial frontend architecture decisions, not just consuming patterns set by others. Production experience building TypeScript/Node.js backend services (Express, Bun, Koa, Fastify, NestJS, or equivalent) with end-to-end type-safe API contracts (tRPC, Zod, OpenAPI codegen, or similar). Comfortable owning the boundary between TypeScript frontends and TypeScript and/or Python backends. Familiarity with the modern TypeScript tooling stack: yarn, pnpm, or npm workspaces, ESLint, Prettier, Vitest or Jest, and build tooling (Vite, Turbopack, esbuild). Treats type-safety, linting, and testing as production requirements, not optional polish. Experience building product experiences for workflows such as tables, document-centric interfaces, review flows, or real-time/streaming interactions. Understanding of UX patterns for AI systems, including confidence indicators, citations/source grounding, fallback states, edit/retry patterns, and human review steps. Good product sense in translating non-deterministic AI behavior into usable and trustworthy product experiences. Working Python proficiency as a strong co-requirement (secondary to TypeScript), including FastAPI, Pydantic v2, async patterns, and pytest. Expectation: comfortable contributing to and reviewing Python services and LangGraph/agent service work. Hands-on experience with LangChain and/or LangGraph, including stateful graph construction, tool integration, checkpointing, and streaming patterns. Familiarity with Google ADK or equivalent agentic orchestration frameworks is a plus. Exposure to Temporal or similar durable execution frameworks, including workflow/activity authoring and retry patterns. Prompt engineering skills, including structured output design, system prompt construction, instruction clarity, and multi-turn context management. Experience implementing or contributing to RAG pipelines, including chunking, embedding selection, vector store integration, and retrieval quality evaluation. Familiarity with LLM evaluation approaches, including golden dataset design, metric definition, and regression gate concepts. Awareness of context window management strategies such as token budgeting, truncation, and tool-call state persistence. Familiarity with vector databases such as pgvector and/or OpenSearch. Experience with Docker and familiarity with Kubernetes deployment concepts. Generative AI and Agentic Systems Uses AI coding assistants such as Cursor and GitHub Copilot as part of the development workflow, while applying judgement about where generated code is reliable versus where it requires scrutiny. Familiarity with multi-agent system concepts including orchestration logic, tool interfaces, and failure-handling patterns. Capable of contributing to evaluation pipelines that combine deterministic metrics with LLM-as-judge patterns for qualitative assessment. Able to review AI-generated code, including Kubernetes manifests, prompts, and agent graphs, for correctness and safety before production release. General Understands non-determinism as a first-class engineering challenge and contributes to systems that degrade gracefully when model outputs are unexpected. Writes evaluation tests before shipping new AI capabilities, not after. Documents behavior specifications, tool contracts, and user-facing interaction patterns rather than leaving critical behavior implicit in code. This role follows a hybrid model, requiring in-office presence at least 1 day per week.

U.S. COMPENSATION INFORMATION

Compensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best-in-class benefits package (details listed below). In Atlanta, the good-faith, reasonable annualized full-time salary range for this role is between $79,250 - $86,500. In Texas, the good-faith, reasonable annualized full-time salary range for this role is between $83,000 - $90,750. In Chicago, the good-faith, reasonable annualized full-time salary range for this role is between $87,000 - $95,250. Placement within these ranges will vary based on factors such as experience, education, training, and skill level. Compensation also includes a discretionary annual performance bonus, 401(k) plan with employer contribution, and Bain’s best-in-class benefits—including full premium coverage for medical, dental, and vision, generous paid time off, and more. Annual discretionary performance bonus. This role may also be eligible for other elements of discretionary compensation. 4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date. Bain & Company's comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family. Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheck. Generous paid time off, including parental leave, sick leave and paid holidays. Fully vested 401(k) company contribution. Paid Life and Long-Term Disability insurance. #J-18808-Ljbffr Bain & Company