Platform Engineering

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.

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.

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 includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses.

Platform Engineers build and maintain the shared backend services that power the Bain Diligence Platform (BDP). These include enterprise SSO, session management, audit, case and case‑provenance APIs, document and file access (Microsoft Graph / SharePoint and Azure Blob Storage), search with source citations, and the API gateway. You contribute to services across the full delivery lifecycle — design, build, test, deploy, monitor, and operate — with guidance from senior engineers. You write reliable, well‑tested code, follow platform engineering standards, and help ensure services are secure‑by‑default, observable, and production‑ready. You collaborate closely with Platform, Data & Intelligence, Security, and Infrastructure teammates to deliver shared capabilities that are stable, scalable, and easy for squads to consume as the platform hardens.

Core Platform Service Development, Deployment, and Operations (80%)

Implement backend features and service endpoints in existing platform services under the guidance of senior engineers.

Use Redis for standard platform patterns such as caching, session storage, and rate limiting under established conventions.

Use event‑driven integration where asynchronous behaviour adds clear value (for example change feeds and domain events, following existing schema and envelope conventions), consistent with the platform's simple‑now, evolve‑when‑needed approach.

Add and maintain service instrumentation: structured logs, OpenTelemetry‑style spans, and Datadog metrics and APM traces according to platform standards.

ensure probes, resource requests/limits, and environment configuration are correct, and support blue/green candidate/promote deployments.

Contribute to Terraform infrastructure‑as‑code for Azure, deployed through env0 / Terraform Cloud across dev, UAT, and prod environments, with review support.

Work within GitHub Actions pipelines: pull‑request test suites, branch‑to‑environment promotion (main → Dev, release/X.Y → UAT/Prod), image publishing to Cloudsmith, and blue/green candidate/promote deploys with approval gates.

participate in on‑call rotation with support and follow established runbooks.

Write and update runbooks and service documentation for changes you ship, ensuring operational steps remain accurate.

Use AI coding assistants to accelerate boilerplate creation (routers, schemas, tests, docs); review all generated code and align it with platform conventions before committing.

Use LLMs to draft first‑pass documentation (runbook updates, service notes, API docs); Collaborate with Security and Infrastructure on implementation tasks (e.g., Azure Key Vault secret consumption, Entra ID app registrations, network‑security‑group / private‑endpoint constraints, least‑privilege configuration, and Wiz security‑scan findings) under guidance.

Contribute to quality improvements (test coverage, refactors, observability, and reliability fixes) as part of regular delivery work.

Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field (or equivalent practical experience)

2+ years of experience building backend services, APIs, or platform components in a production or near‑production environment.

Demonstrated experience building and maintaining backend services or APIs with an emphasis on reliability and correctness.

Experience contributing to REST APIs and working with relational databases (Postgres preferred), including basic schema evolution and migration workflows.

Exposure to containerised development and deployment practices (Docker) and familiarity with operating services in Kubernetes environments (Azure / AKS in practice).

Exposure to authentication and authorisation concepts (JWT, RBAC, OIDC/SAML — Entra ID / Azure AD in practice) and interest in building secure‑by‑default services.

Backend / platform engineering

Proficient Python development skills; Docker proficiency for local development and building container images; understands non‑root execution and basic image hygiene.

Kubernetes fundamentals: understands deployments/services, pod lifecycle basics, and how health checks and resource limits affect service behaviour; able to work with Helm values and charts on AKS, including blue/green deployments.

Observability fundamentals: structured logging, tracing concepts, and emitting metrics and APM traces via Datadog for service health; awareness of MLflow for evaluating AI features.

understands environment promotion, container registries (Cloudsmith), Helm chart versioning, and blue/green deployments on AKS.

Generative AI and agentic systems

Uses AI coding assistants (Cursor, GitHub Copilot, or equivalent) to accelerate development tasks (boilerplate, refactors, basic tests); Uses LLMs to draft first‑pass unit tests and documentation; validates correctness, adds missing edge cases, and ensures alignment with platform standards.

Understands how core platform services (SSO/JWT authentication, RBAC, audit, and case provenance) underpin agentic systems, and why access control, auditability, and provenance matter for AI interactions — including the platform's “skills orchestrate, agents execute” model.

Familiarity with the LLM‑application patterns used on the platform: an LLM gateway (Portkey) for model access and routing, structured / tool‑constrained outputs with schema validation and automatic repair, and retrieval‑grounded answers with citations back to source.

Exposure to eval‑driven development for AI features — building evaluation cases and using MLflow to measure quality, regressions, and repair uplift before promoting changes.

Raises risks early (unclear requirements, security concerns, migration risk, operational impact) and seeks help appropriately.

Uses AI tooling to move faster, but applies judgement and careful review to all generated code and documentation before it enters the codebase.

Maintains a strong ownership mindset for assigned work: follows through from implementation to deployment support and post‑release verification.

Favours the simplest solution that meets the need — introducing new components, services, or infrastructure only when a concrete requirement justifies it, and keeping services secure‑by‑default, observable, versioned, and backward‑compatible.

This role follows a hybrid model, requiring in‑office presence at least 1 day per week

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).

Some local governments in the United States require a good‑faith, reasonable salary range be included in job postings for open roles. In Atlanta, the good‑faith, reasonable annualized full‑time salary range for this role is between $64,000 - $76,750

In Texas, the good‑faith, reasonable annualized full‑time salary range for this role is between $67,000 - $80,500

In Chicago, the good‑faith, reasonable annualized full‑time salary range for this role is between $70,500 - $84,500

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
Annual discretionary performance bonus

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 & 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
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
Generous paid time off, including parental leave, sick leave and paid holidays

Paid Life and Long-Term Disability insurance
Paid Life and Long-Term Disability insurance

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