Description
At BlackRock, Technology underpins everything we do, and Artificial Intelligence (AI) is a strategic priority across Aladdin and our investment, client, and operational platforms.
As an AI Infrastructure Engineer, you will design, build, and operate the foundational platforms that support AI workloads throughout their lifecycle, from development and experimentation through training, evaluation, deployment, and inference.
You will help create scalable, reliable production environments while embedding security, governance, observability, automation, and operational resilience into the platform.
Responsibilities
- Design, build, and operate scalable, reliable, and secure cloud-native infrastructure supporting AI model development, training, evaluation, deployment, and inference across public cloud and hybrid environments.
- Partner with AI Engineering, Data Science, Platform Engineering, and Product teams to improve developer experience, platform performance, and operational stability while contributing to AI platform architecture and technical standards.
- Enable production deployment of machine learning models and large language models within governed enterprise environments, integrating identity, access controls, data protection, security, and risk requirements.
- Implement Infrastructure as Code (IaC) and automation to enable repeatable, consistent, and auditable platform provisioning and configuration.
- Build and operate observability, monitoring, and alerting capabilities that provide visibility into AI platform availability, performance, reliability, capacity, and cost.
- Participate in operational support and on-call rotations for critical platforms, contributing to incident response and resilient production services.
- Evaluate emerging AI infrastructure technologies and engineering approaches, applying capabilities appropriate to BlackRock’s enterprise environment.
Experience
- Demonstrated experience in cloud infrastructure, platform engineering, systems engineering, or a closely related discipline, including designing and operating services across major public cloud and hybrid environments.
- Experience supporting AI and machine learning platforms across the development and production lifecycle, including training, fine-tuning, experimentation, validation, model deployment and serving, lifecycle management, observability, resilience, disaster recovery, and end-to-end Machine Learning Operations (MLOps) pipelines.
- Proficiency with Infrastructure as Code, cloud infrastructure provisioning, and automation, supported by programming or scripting capability.
- Experience building and operating enterprise-grade containerized platforms that support declarative infrastructure and horizontal scaling, with a strong understanding of reliability, scalability, observability, and production engineering practices.
- Familiarity with graphics processing unit (GPU) or other accelerator-based AI infrastructure, multicloud architectures, and enterprise governance requirements.
- Ability to collaborate effectively across engineering, security, risk, data, and product teams and communicate complex technical concepts clearly.
Benefits
- Retirement investment and tools designed to help you in building a sound financial future;
- Access to education reimbursement;
- Comprehensive resources to support your physical health and emotional well-being;
- Family support programs;
- Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about.