AI engineer

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.

• Contribute to the provisioning and management of cloud infrastructure for the Growth AI Performance System platform on Azure or AWS (including compute, networking, storage, and managed services), using infrastructure-as-code practices under the direction of the Senior DevOps Engineer
• Support the maintenance of environment parity across development, staging, and production; contribute to environment provisioning, configuration management, and the automation that keeps environments consistent and reliable
• Build and maintain infrastructure automation scripts and configuration management tooling; learn and apply infrastructure-as-code practices to all infrastructure changes, ensuring changes are versioned, reviewed, and deployed through code
• Contribute to the design and implementation of infrastructure for AI platform workloads: container orchestration, API management, and the storage and compute patterns that LLM services and agentic workflows require
• Build and maintain CI/CD pipeline components for Growth AI Performance System platform services (including AI backend services, frontend applications, and data pipelines), implementing automated testing steps, deployment stages, and quality gates under the direction of the Senior DevOps Engineer
• Maintain the local development environment and developer onboarding tooling; keep documentation current, resolve developer environment issues quickly, and contribute to the developer experience improvements that keep a distributed engineering team productive
• Support release engineering operations: contribute to deployment coordination, monitor deployments in progress, and participate in rollback and incident response procedures
• Learn and develop fluency with the full CI/CD toolchain and deployment workflow; take on progressively more ownership of specific pipeline areas as your expertise grows
• Implement and maintain monitoring and alerting for platform services: set up structured logging, metrics collection, and alerting thresholds that give the engineering team visibility into service health and surface issues before they affect live client deployments
• Contribute to the build of AI and agentic observability tooling under the direction of the Senior DevOps Engineer: implementing tracing for agentic execution, monitoring LLM API usage and latency, and structured logging for multi-step agent workflows
• Support incident response for infrastructure and platform failures: monitor alerts, participate in diagnosis, contribute to remediation, and document findings in a form that supports post-incident review and platform improvement
• Develop familiarity with the platform's SLOs, alerting thresholds, and reliability standards; learn to read platform health dashboards accurately and understand what degradation signals require escalation

• Solid foundation in cloud infrastructure on Azure or AWS: compute, networking, storage, and managed services; growing proficiency with provisioning and managing cloud resources through infrastructure-as-code
• Working experience with CI/CD pipelines and deployment automation: able to build, maintain, and debug pipeline stages including automated testing, artifact management, and deployment steps
• Proficiency with Docker and containerized deployments; working familiarity with Kubernetes-based orchestration in a production environment
• Proficiency in Linux administration and scripting (Python and Bash) for infrastructure automation tasks; disciplined use of version control for infrastructure codebases
• Working knowledge of monitoring and alerting tooling: able to set up structured logging, configure metrics collection, and create alerting rules that surface meaningful signals
• Exposure to or genuine interest in MLOps and AI infrastructure patterns, how LLM services are deployed and monitored, and what the infrastructure requirements of agentic systems look like in production
• Understanding of security fundamentals for cloud-native infrastructure: IAM, secrets management, network security, and the DevSecOps practices that embed security into deployment workflows
• Active use of AI-assisted development tools to accelerate infrastructure work, diagnose issues faster, and develop AI infrastructure skills
• 3 or more years in DevOps, platform engineering, or infrastructure engineering roles with hands-on experience building and operating CI/CD pipelines and cloud infrastructure
• Track record of contributing to production infrastructure and deployment systems that real services and users depend on, not just development or test environments
• Experience working in agile, cross-functional product teams; strong troubleshooting skills and comfort debugging infrastructure issues across distributed systems
• 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
• Будет плюсом: Experience with or strong interest in MLOps and AI infrastructure; exposure to LLM service deployment, agentic system infrastructure, or vector database workloads is a strong advantage

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