{"schemaVersion":"jobsearcher.job.v1","id":"186d35134af5357db875d379","url":"https://jobsearcher.com/jobs/186d35134af5357db875d379","canonicalUrl":"https://jobsearcher.com/jobs/186d35134af5357db875d379","title":"Full Stack TypeScript Engineer, AI Products (DataEdge)","description":"WHAT MAKES US A GREAT PLACE TO WORK\n\nWe are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times.\n\nExtraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.\n\nWHO YOU’LL WORK WITH\n\nAs 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.\n\nBain & 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.\n\nThe 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 includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses.\n\nWHERE YOU’LL FIT WITHIN THE TEAM\n\nFull-Stack TypeScript Engineer, AI Products build and deliver end-to-end AI product experiences across the PE due diligence ecosystem. 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.\n\nThis 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 pieces within our backend layer (LangGraph, FastAPI, agent services), so candidates must be comfortable working productively across both ecosystems even where TypeScript is their primary depth.\n\nYou 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.\n\nYou will contribute to engineering standards, participate in code reviews, and grow your expertise in building safe, observable, and scalable AI-powered workflows.\n\nWHAT YOU'LL DO\n\nFull-Stack AI Product Engineering (65%)\n\nBuild end-to-end AI product features across TypeScript frontend experiences, TypeScript/Node.js backend services, and Python-based AI orchestration layers.\nDevelop analyst-facing and internal AI interfaces for workflows such as deal screening, commercial due diligence research, document extraction, and portfolio monitoring.\nBuild 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.\nBuild 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.\nImplement full-stack application patterns for chat, copilot, workspace, and review-based AI experiences, including state management, real-time updates, and error handling.\nCollaborate with Product, Design, and domain stakeholders to translate AI capabilities into intuitive, polished user experiences.\nDesign and maintain end-to-end type-safe contracts between TypeScript frontends and TypeScript/Python backend services, ensuring outputs are structured, testable, and resilient.\nSupport contribution workflows and product surfaces for the Prompt Execution Sandbox and AI Artifact Studio, enabling safe and scalable use by non-engineers where required.\nEnsure AI product features are accessible, observable, and production-ready, with attention to usability, reliability, and edge-case handling.\nAI Platform and Agent Workflow Engineering (35%)\n\nContribute 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.\nBuild and maintain LangGraph agent workflows for PE use cases, including streaming, tool-calling, multi-step execution, and human-in-the-loop interrupt patterns.\nIntegrate Temporal durable execution with LangGraph, including workflow and activity authoring, checkpointing strategies, retry and backoff policies, and signal/query handling.\nContribute to AI platform services such as Agent Session Manager, Memory Service, HITL Coordination Service, and Feedback/Correction Service.\nImplement RAG pipelines, including chunking strategies, embedding model selection, vector store integration, re-ranking, and retrieval quality evaluation.\nSupport evaluation and regression gates, including golden dataset management, metric definition, qualitative and quantitative evaluation, and CI enforcement on quality regressions.\nImplement context window management strategies such as token budgeting, truncation/compression, and tool-call state persistence to support reliability in longer-running workflows.\nInstrument 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.\nSupport deployment and operation of AI workloads in Azure, including containerization deployment patterns.\nCollaboration and Engineering Standards\n\nParticipate 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.\nCollaborate with Data Platform on feature store access patterns, inference integration, schemas, and data contracts.\nWork with Product Engineering and Design on AI feature surfacing, including streaming experiences, structured output rendering, citation and evidence UX, and HITL review interfaces.\nUse AI coding assistants to accelerate prototyping and development, while validating all production artifacts against testing and evaluation gates before promotion.\nDocument agent behavior specifications, tool contracts, and product interaction patterns so behavior is explicit, reviewable, and maintainable.\nABOUT YOU\n\nBachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent practical experience.\nDemonstrable 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.\n3+ years of experience building production software, including experience delivering full-stack applications and/or AI-enabled systems in production environments.\nExperience contributing to user-facing AI product features, from backend services through frontend implementation.\nExperience working with agentic systems in production or pre-production, including tool calling, multi-step workflows, RAG, or structured output handling.\nExposure to evaluation frameworks, including golden datasets, regression gates, or CI controls for quality assurance.\nExperience working with containerized environments such as Docker and Kubernetes, including familiarity with monitoring and reliability practices.\nFull-Stack Product Engineering\n\nExperience building modern full-stack applications with frontend architecture and backend integration.\nStrong 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.\nProduction 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.\nStrong 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.\nPractical PostgreSQL depth: query plan analysis, JSONB usage patterns, and comfortable authoring and maintaining triggers and stored procedures where they’re the right tool.\nComfortable owning schema evolution end-to-end: write safe, backwards-compatible migrations and reasons about migration safety in a multi-service production environment.\nFamiliarity 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.\nExperience building product experiences for workflows such as tables, document-centric interfaces, review flows, or real-time/streaming interactions.\nUnderstanding of UX patterns for AI systems, including confidence indicators, citations/source grounding, fallback states, edit/retry patterns, and human review steps.\nGood product sense in translating non-deterministic AI behavior into usable and trustworthy product experiences.\nAI Platform Engineering\n\nWorking 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.\nHands-on experience with LangChain and/or LangGraph, including stateful graph construction, tool integration, checkpointing, and streaming patterns.\nFamiliarity with Google ADK or equivalent agentic orchestration frameworks is a plus.\nExposure to Temporal or similar durable execution frameworks, including workflow/activity authoring and retry patterns.\nPrompt engineering skills, including structured output design, system prompt construction, instruction clarity, and multi-turn context management.\nExperience implementing or contributing to RAG pipelines, including chunking, embedding selection, vector store integration, and retrieval quality evaluation.\nFamiliarity with LLM evaluation approaches, including golden dataset design, metric definition, and regression gate concepts.\nAwareness of context window management strategies such as token budgeting, truncation, and tool-call state persistence.\nFamiliarity with vector databases such as pgvector and/or OpenSearch.\nExperience with Docker and familiarity with Kubernetes deployment concepts.\nGenerative AI and Agentic Systems\n\nUses 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.\nFamiliarity with multi-agent system concepts including orchestration logic, tool interfaces, and failure-handling patterns.\nCapable of contributing to evaluation pipelines that combine deterministic metrics with LLM-as-judge patterns for qualitative assessment.\nAble to review AI-generated code, including Kubernetes manifests, prompts, and agent graphs, for correctness and safety before production release.\nGeneral\n\nUnderstands non-determinism as a first-class engineering challenge and contributes to systems that degrade gracefully when model outputs are unexpected.\nWrites evaluation tests before shipping new AI capabilities, not after.\nPrototypes quickly using AI tooling, but validates production artifacts against defined quality gates before promotion.\nDocuments behavior specifications, tool contracts, and user-facing interaction patterns rather than leaving critical behavior implicit in code.\nThis role follows a hybrid model, requiring in-office presence at least 1 day per week\nU.S. COMPENSATION INFORMATION\n\nCompensation 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).\n\nSome local governments in the United States require a good-faith, reasonable salary range be included in job postings for open roles. The estimated annualized compensation for this role is as follows:\n\nIn Atlanta, the good-faith, reasonable annualized full-time salary range for this role is between $79,250 - $86,500\n\nIn Texas, the good-faith, reasonable annualized full-time salary range for this role is between $83,000 - $90,750\n\nIn Chicago, the good-faith, reasonable annualized full-time salary range for this role is between $87,000 - $95,250\n\nPlacement within these ranges will vary based on factors such as experience, education, training, and skill level.\n\nCompensation 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.\n\nAnnual discretionary performance bonus\n\nThis role may also be eligible for other elements of discretionary compensation\n\n4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date\n\nBain & 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.\n\nBain 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\n\nGenerous paid time off, including parental leave, sick leave and paid holidays\n\nFully vested 401(k) company contribution\n\nPaid Life and Long-Term Disability insurance\n\nAnnual fitness reimbursements","company":"Bain","rawCompany":"bain","city":"Chicago","state":"IL","isRemote":false,"isActive":false,"createdAt":"2026-07-29T11:49:33.110Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1254.00","title":"Web Developers","slug":"web-developers"},{"code":"17-2199.00","title":"Engineers, All Other","slug":"engineers-all-other"}],"industries":[{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Full Stack TypeScript Engineer, AI Products (DataEdge)","description":"WHAT MAKES US A GREAT PLACE TO WORK\n\nWe are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times.\n\nExtraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.\n\nWHO YOU’LL WORK WITH\n\nAs 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.\n\nBain & 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.\n\nThe 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 includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses.\n\nWHERE YOU’LL FIT WITHIN THE TEAM\n\nFull-Stack TypeScript Engineer, AI Products build and deliver end-to-end AI product experiences across the PE due diligence ecosystem. 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.\n\nThis 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 pieces within our backend layer (LangGraph, FastAPI, agent services), so candidates must be comfortable working productively across both ecosystems even where TypeScript is their primary depth.\n\nYou 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.\n\nYou will contribute to engineering standards, participate in code reviews, and grow your expertise in building safe, observable, and scalable AI-powered workflows.\n\nWHAT YOU'LL DO\n\nFull-Stack AI Product Engineering (65%)\n\nBuild end-to-end AI product features across TypeScript frontend experiences, TypeScript/Node.js backend services, and Python-based AI orchestration layers.\nDevelop analyst-facing and internal AI interfaces for workflows such as deal screening, commercial due diligence research, document extraction, and portfolio monitoring.\nBuild 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.\nBuild 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.\nImplement full-stack application patterns for chat, copilot, workspace, and review-based AI experiences, including state management, real-time updates, and error handling.\nCollaborate with Product, Design, and domain stakeholders to translate AI capabilities into intuitive, polished user experiences.\nDesign and maintain end-to-end type-safe contracts between TypeScript frontends and TypeScript/Python backend services, ensuring outputs are structured, testable, and resilient.\nSupport contribution workflows and product surfaces for the Prompt Execution Sandbox and AI Artifact Studio, enabling safe and scalable use by non-engineers where required.\nEnsure AI product features are accessible, observable, and production-ready, with attention to usability, reliability, and edge-case handling.\nAI Platform and Agent Workflow Engineering (35%)\n\nContribute 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.\nBuild and maintain LangGraph agent workflows for PE use cases, including streaming, tool-calling, multi-step execution, and human-in-the-loop interrupt patterns.\nIntegrate Temporal durable execution with LangGraph, including workflow and activity authoring, checkpointing strategies, retry and backoff policies, and signal/query handling.\nContribute to AI platform services such as Agent Session Manager, Memory Service, HITL Coordination Service, and Feedback/Correction Service.\nImplement RAG pipelines, including chunking strategies, embedding model selection, vector store integration, re-ranking, and retrieval quality evaluation.\nSupport evaluation and regression gates, including golden dataset management, metric definition, qualitative and quantitative evaluation, and CI enforcement on quality regressions.\nImplement context window management strategies such as token budgeting, truncation/compression, and tool-call state persistence to support reliability in longer-running workflows.\nInstrument 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.\nSupport deployment and operation of AI workloads in Azure, including containerization deployment patterns.\nCollaboration and Engineering Standards\n\nParticipate 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.\nCollaborate with Data Platform on feature store access patterns, inference integration, schemas, and data contracts.\nWork with Product Engineering and Design on AI feature surfacing, including streaming experiences, structured output rendering, citation and evidence UX, and HITL review interfaces.\nUse AI coding assistants to accelerate prototyping and development, while validating all production artifacts against testing and evaluation gates before promotion.\nDocument agent behavior specifications, tool contracts, and product interaction patterns so behavior is explicit, reviewable, and maintainable.\nABOUT YOU\n\nBachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent practical experience.\nDemonstrable 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.\n3+ years of experience building production software, including experience delivering full-stack applications and/or AI-enabled systems in production environments.\nExperience contributing to user-facing AI product features, from backend services through frontend implementation.\nExperience working with agentic systems in production or pre-production, including tool calling, multi-step workflows, RAG, or structured output handling.\nExposure to evaluation frameworks, including golden datasets, regression gates, or CI controls for quality assurance.\nExperience working with containerized environments such as Docker and Kubernetes, including familiarity with monitoring and reliability practices.\nFull-Stack Product Engineering\n\nExperience building modern full-stack applications with frontend architecture and backend integration.\nStrong 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.\nProduction 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.\nStrong 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.\nPractical PostgreSQL depth: query plan analysis, JSONB usage patterns, and comfortable authoring and maintaining triggers and stored procedures where they’re the right tool.\nComfortable owning schema evolution end-to-end: write safe, backwards-compatible migrations and reasons about migration safety in a multi-service production environment.\nFamiliarity 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.\nExperience building product experiences for workflows such as tables, document-centric interfaces, review flows, or real-time/streaming interactions.\nUnderstanding of UX patterns for AI systems, including confidence indicators, citations/source grounding, fallback states, edit/retry patterns, and human review steps.\nGood product sense in translating non-deterministic AI behavior into usable and trustworthy product experiences.\nAI Platform Engineering\n\nWorking 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.\nHands-on experience with LangChain and/or LangGraph, including stateful graph construction, tool integration, checkpointing, and streaming patterns.\nFamiliarity with Google ADK or equivalent agentic orchestration frameworks is a plus.\nExposure to Temporal or similar durable execution frameworks, including workflow/activity authoring and retry patterns.\nPrompt engineering skills, including structured output design, system prompt construction, instruction clarity, and multi-turn context management.\nExperience implementing or contributing to RAG pipelines, including chunking, embedding selection, vector store integration, and retrieval quality evaluation.\nFamiliarity with LLM evaluation approaches, including golden dataset design, metric definition, and regression gate concepts.\nAwareness of context window management strategies such as token budgeting, truncation, and tool-call state persistence.\nFamiliarity with vector databases such as pgvector and/or OpenSearch.\nExperience with Docker and familiarity with Kubernetes deployment concepts.\nGenerative AI and Agentic Systems\n\nUses 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.\nFamiliarity with multi-agent system concepts including orchestration logic, tool interfaces, and failure-handling patterns.\nCapable of contributing to evaluation pipelines that combine deterministic metrics with LLM-as-judge patterns for qualitative assessment.\nAble to review AI-generated code, including Kubernetes manifests, prompts, and agent graphs, for correctness and safety before production release.\nGeneral\n\nUnderstands non-determinism as a first-class engineering challenge and contributes to systems that degrade gracefully when model outputs are unexpected.\nWrites evaluation tests before shipping new AI capabilities, not after.\nPrototypes quickly using AI tooling, but validates production artifacts against defined quality gates before promotion.\nDocuments behavior specifications, tool contracts, and user-facing interaction patterns rather than leaving critical behavior implicit in code.\nThis role follows a hybrid model, requiring in-office presence at least 1 day per week\nU.S. COMPENSATION INFORMATION\n\nCompensation 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).\n\nSome local governments in the United States require a good-faith, reasonable salary range be included in job postings for open roles. The estimated annualized compensation for this role is as follows:\n\nIn Atlanta, the good-faith, reasonable annualized full-time salary range for this role is between $79,250 - $86,500\n\nIn Texas, the good-faith, reasonable annualized full-time salary range for this role is between $83,000 - $90,750\n\nIn Chicago, the good-faith, reasonable annualized full-time salary range for this role is between $87,000 - $95,250\n\nPlacement within these ranges will vary based on factors such as experience, education, training, and skill level.\n\nCompensation 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.\n\nAnnual discretionary performance bonus\n\nThis role may also be eligible for other elements of discretionary compensation\n\n4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date\n\nBain & 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.\n\nBain 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\n\nGenerous paid time off, including parental leave, sick leave and paid holidays\n\nFully vested 401(k) company contribution\n\nPaid Life and Long-Term Disability insurance\n\nAnnual fitness reimbursements","datePosted":"2026-07-29T11:49:33.110Z","dateModified":"2026-07-29T11:49:33.110Z","hiringOrganization":{"@type":"Organization","name":"Bain","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Chicago","addressRegion":"IL","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"186d35134af5357db875d379"},"url":"https://jobsearcher.com/jobs/186d35134af5357db875d379"}}