Staff Engineer I, DevOps and Assurance
Description & Requirements
What makes us a great place to work
We are proud to be consistently recognized as one of the world's best places to work, a champion of diversity and a model of social responsibility. We are currently ranked the number one consulting firm on Glassdoor's Best Places to Work list and have maintained a spot in the top four since its founding in 2009. Extraordinary teams are at the heart of our business strategy, but these do not 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.
Who you'll work with
You will be part of a team building Bain's integrated suite of agentic decisioning tools that gives large organizations always-on intelligence across their most important growth decisions. The role sits at an intersection few organizations can offer: the analytical depth and client access of a top-tier consultancy, the technical ambition of a product company, and engineering work at the frontier of applied AI.
You will work inside Bain's standing product team, building software that helps the world's largest companies answer the questions that drive their business: where to invest, where to focus, and how to win. The impact of what you build is direct and immediate, and the platform compounds in value with every release.
The platform has two layers working together: an agentic core and a set of decision modules, each solving a distinct commercial problem. The build lifecycle is connected to the real world: as case teams work alongside clients across industries and geographies, that feedback flows straight back to engineering and into the product. Every client engagement makes the platform smarter, broader, and more resilient.
This is a startup inside an established company. You get genuine early-stage ownership and real influence over architecture and product direction, backed by Bain's global scale, client relationships, and leadership commitment; the pace of a startup with the runway of a global firm.
We are looking for DevOps engineers who see how to make the platform more reliable and resilient and bring that instinct to every sprint without being asked, and who are comfortable with problems that do not have a known answer. The work involves cloud-native infrastructure, CI/CD for a multi-service platform, and observability problems with no off-the-shelf answer. Experience supporting agentic systems matters: how AI backend services and agentic workflows are deployed and monitored, evaluation pipelines, and the operational demands of production LLM workloads. This is applied AI in production, at scale, with real users depending on what you build. The field is moving quickly and the team is built to move with it, so the willingness to keep learning counts for as much as what you already know.
Where you'll fit within the team
The Senior DevOps Engineer owns the cloud infrastructure, deployment pipelines, and platform observability for the entire Growth AI Performance System platform: the foundation that every AI service, product interface, data pipeline, and agentic workflow runs on. This is a hands-on platform engineering role: you will design, provision, and operate the infrastructure that real client deployments depend on, and you will own the CI/CD and release engineering processes that allow the engineering team to ship reliably and safely at pace.
DevOps for an AI platform has requirements that go beyond conventional software infrastructure. You will build observability tooling for agentic execution, tracing multi-step agent workflows, monitoring LLM API latency and cost, and surfacing the kinds of failures that only become visible when agents interact with real client data in production. This is a frontier area of platform engineering, and this role is expected to develop and own it.
You will work closely with the Security Engineer on infrastructure hardening, IAM, secrets management, and the security controls embedded in the CI/CD pipeline. You will work with the AI Engineering team on how AI services are deployed, scaled, and monitored. And you will own the local development environment and deployment documentation that keeps a globally distributed engineering team productive.
This role is based in Warsaw or Sao Paulo, working as part of a globally distributed platform team. You will mentor the g6 DevOps Engineer, providing technical direction without formal people-management responsibility, set platform engineering standards, and be the senior technical voice on infrastructure architecture decisions.
What you'll do
Infrastructure design and provisioning
- Design, provision, and manage cloud-based distributed infrastructure for the Growth AI Performance System platform across Azure or AWS (including compute, networking, storage, managed databases, container orchestration, and API management), applying infrastructure-as-code practices throughout
- Maintain full environment parity across development, staging, and production; ensure that the infrastructure engineers develop against is the infrastructure that client deployments run on, and that promotions between environments are automated, auditable, and reliable
- Design infrastructure for the specific requirements of AI platform services: variable and spiky compute demand for LLM API calls, low-latency requirements for agentic workflow steps, and the storage and retrieval patterns of knowledge graph and vector database workloads
- Work with the Security Engineer to ensure infrastructure is hardened against the threat model relevant to an AI platform serving enterprise client data: network segmentation, IAM, secrets management, and security controls embedded in infrastructure-as-code
- Stay current with cloud infrastructure tooling and AI infrastructure patterns; evaluate and adopt new platform capabilities that improve reliability, reduce cost, or enable engineering team velocity
CI/CD and release engineering
- Build and optimize CI/CD pipelines for all Growth AI Performance System platform services (AI backend services, frontend applications, data pipelines, and infrastructure changes), embedding automated testing, security scanning, and quality gates that make deployment safe and fast
- Own release engineering and deployment sequencing across the platform: coordinate releases across services with dependencies, manage rollback procedures, and ensure that platform and product module releases remain aligned in production and client-facing environments
- Build and maintain infrastructure-as-code for all platform environments using modern IaC tooling; ensure infrastructure changes are versioned, reviewed, and deployed through the same pipeline discipline as application code
- Maintain the local development environment and developer tooling: onboarding documentation, local service configuration, and the developer experience infrastructure that keeps a globally distributed engineering team productive without infrastructure friction
Observability and AI platform monitoring
- Implement observability across all platform services: structured logging, metrics, distributed tracing, and alerting that gives the engineering team visibility into service health, API latency, pipeline reliability, and agentic execution behavior
- Build specialized observability tooling for agentic AI workloads: tracing multi-step agent execution, monitoring LLM API usage and cost, surfacing context passing failures, and detecting the kinds of agentic system failures that conventional monitoring does not capture
- Monitor platform performance proactively: identify degradation before it affects live client deployments, diagnose root causes across distributed services, and drive the engineering improvements that resolve them
- Define and maintain SLOs and alerting thresholds for all platform services; lead incident response for infrastructure and platform failures and conduct structured post-incident reviews that improve platform reliability over time
About you
Technical
- Strong cloud infrastructure engineering experience across Azure or AWS: compute, networking, storage, managed databases, container orchestration, and API management at production scale
- Deep proficiency with infrastructure-as-code tooling; experience provisioning and managing complex, multi-environment cloud infrastructure through code rather than manual configuration
- Strong CI/CD engineering experience: building and operating deployment pipelines for multi-service platforms including AI backend services, frontend applications, and data pipelines
- Practitioner-level Kubernetes experience: deploying, operating, and troubleshooting containerized workloads in production environments with real latency and reliability requirements
- Experience building observability for distributed systems: structured logging, metrics, distributed tracing, and alerting using modern observability platforms; growing expertise in AI and agentic system observability
- Experience with MLOps and AI infrastructure patterns: how LLM API services are deployed, scaled, and monitored; familiarity with the infrastructure requirements of vector databases, knowledge graph workloads, and agentic execution
- Understanding of security and compliance requirements for cloud-native platforms serving enterprise client data: IAM, secrets management, network security, and the DevSecOps practices that embed security into the deployment pipeline
- Proficiency in Linux administration and scripting for infrastructure automation; disciplined use of version control for infrastructure codebases
- Active use of AI-assisted tooling to accelerate infrastructure development, diagnose issues faster, and develop AI infrastructure expertise as the platform scales
Experience
- 5 or more years in DevOps, platform engineering, or site reliability engineering roles with hands-on responsibility for production cloud infrastructure
- Track record of owning CI/CD and infrastructure for a multi-service platform end-to-end, not just contributing to pipelines, but designing, building, and operating them with real reliability requirements
- Experience with MLOps or AI platform infrastructure (supporting LLM services, agentic systems, or ML pipelines in production) is a strong advantage
- Experience mentoring junior engineers and setting platform engineering standards within a team
- Experience working in agile, cross-functional product or consulting-adjacent environments; strong troubleshooting and incident response skills
- Based in or able to work from Warsaw or Sao Paulo near a Bain office
Education
- Associate’s degree / bachelor's degree in computer science, engineering, mathematics, or a related technical field; or an equivalent combination of education, training, and experience