Full Stack TypeScript Engineer, AI Products (DataEdge)

About the role

As the premier consulting partner for the private equity industry, Bain's PEG boasts a global practice that is over three times larger than any competitor. Our network of over 1,000 professionals supports private equity and institutional investor clients through every stage of the investment life cycle, from deal generation and due diligence to portfolio value creation and exit planning.

Bain & Company is developing a suite of cutting-edge data and software solutions designed to revolutionize how the private equity industry uses data for investment insights and decision-making. The PEG Innovation team's mission is to create analytical solutions for Bain clients, teams, and the broader institutional investor space using proprietary software and data products.

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.

Responsibilities

• Full-Stack AI Product Engineering (65%)
  • Build end-to-end AI product features across TypeScript frontend experiences, TypeScript/Node.js backend services, and Python-based AI orchestration layers.
  • 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 in TypeScript (Svelte, React, Next.js, or similar) for streaming AI responses, structured outputs, source grounding, review and approval flows, and human-in-the-loop interactions.
  • Build and operate TypeScript/Node.js backend services that power AI product features, including API endpoints, orchestration glue between frontend experiences and Python-based agent services, and the data access layer (Postgres) that backs them.
  • 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.
  • Design and maintain end-to-end type-safe contracts between TypeScript frontends and TypeScript/Python 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 (Python) and the TypeScript client libraries that consume it, 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 both TypeScript product surfaces and Python 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 Azure, including containerization deployment patterns.
• Collaboration and Engineering Standards
  • Participate in code reviews and contribute to engineering standards across the TypeScript and Python codebases for production AI product engineering, including 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.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent practical experience.
• 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.
• 3+ years of experience building production software, including experience delivering full-stack applications and/or AI-enabled systems in production environments.
• 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.

Skills

• Full-Stack Product Engineering
  • 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.
  • 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).
  • Strong relational data modeling skills: comfortable writing and reviewing raw SQL, designing normalized schemas, and reasoning about tables, views, indexes, and foreign-key relationships rather than relying solely on ORM-generated patterns.
  • Practical PostgreSQL depth: query plan analysis, JSONB usage patterns, and comfortable authoring and maintaining triggers and stored procedures where they’re the right tool.
  • Comfortable owning schema evolution end-to-end: write safe, backwards-compatible migrations and reasons about migration safety in a multi-service production environment.
  • 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.
• AI Platform Engineering
  • Working Python proficiency as a strong co-requirement (secondary to TypeScript), including FastAPI, Pydantic v2, async patterns, and pytest.
  • 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 workflows, including tool calling, multi-step execution, and human-in-the-loop patterns.