Platform Engineer

Bain & Company•Austin, TX

77d•$64,000 - $84,500•Hybrid

About The Position

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.

Requirements

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.
Demonstrated ability to learn from feedback, collaborate effectively, and deliver production-ready work within established engineering standards.
Proficient Python development skills; able to build FastAPI services using Pydantic, write tests with pytest, and follow linting/type-checking standards (Ruff, mypy), and manage environments and dependencies with uv.
Working knowledge of Postgres fundamentals: schema design basics, indexing concepts, and safe migration practices with Alembic.
Working knowledge of Redis patterns for caching and session storage; understands basic failure modes and operational considerations.
Familiarity with event-driven integration patterns applied where asynchronous behaviour adds value (change feeds / domain events, consumers, retries, idempotency); able to implement producers/consumers within established conventions.
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.
Security fundamentals: understands least-privilege access, secret-handling basics, and common authN/authZ concepts; follows established patterns for JWT and RBAC integration, Entra ID, and Azure Key Vault.
Familiarity with infrastructure-as-code (Terraform) and CI/CD via GitHub Actions; understands environment promotion, container registries (Cloudsmith), Helm chart versioning, and blue/green deployments on AKS.
Uses AI coding assistants (Cursor, GitHub Copilot, or equivalent) to accelerate development tasks (boilerplate, refactors, basic tests); reviews all outputs critically before committing.
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.
Treats every service change as production-impacting: writes tests, adds observability where needed, and updates documentation and runbooks.
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.

Responsibilities

Implement backend features and service endpoints in existing platform services under the guidance of senior engineers.
Build and maintain REST APIs in FastAPI using clear contracts, input validation, and consistent error handling.
Write and update Postgres schemas and Alembic migrations using safe patterns (including backwards-compatible changes) with review support.
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.
Update and maintain Helm chart values and Kubernetes manifests for services deployed to Azure Kubernetes Service (AKS); 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.
Triage and resolve routine production issues for owned services; 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.
Participate in code reviews as both author and reviewer; incorporate feedback and apply engineering standards consistently.
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); validate and refine outputs before publishing.
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.