AI Platform Engineer, Director

BlackRock is one of the world’s leading providers of investment, advisory, and risk management solutions, including Aladdin, our investment and risk management technology. Aladdin is a comprehensive technology platform used by BlackRock and delivered to our clients to unify the investment management process across public and private markets. It integrates risk, investment, and client management processes through a common data language, enabling scale, insights, and business transformation. At BlackRock, the Aladdin team comprises the Aladdin Client Business, Aladdin Wealth Tech, Aladdin Product, Aladdin Data, and Aladdin Engineering. 

Join our AI Platform Engineering team as part of Aladdin Engineering and be at the forefront of financial technology innovation. Aladdin Engineering is the team that designs, builds, and runs Aladdin. Within Aladdin Engineering, there are multiple engineering teams focused on building the different products and platform services for Aladdin. As part of the AI Platform Engineering team, you will play a crucial role in shaping the AI ecosystem across the firm and significantly influencing the Aladdin application ecosystem. 

We are looking for highly motivated and determined engineers to set and drive a clear AI Software Strategy, delivering cohesive AI experiences across the firm. At BlackRock, you will have the opportunity to work with some of the brightest minds in the industry, leveraging your insights and expertise to advance AI Platform Engineering. We value diversity and believe it is the key to our success, ensuring that your unique skills, curiosity, and passion are nurtured. Join us and grow both technically and personally while working at one of the most recognized financial companies in the world as part of an AI software development team. 

Key Responsibilities: 

• Design, develop, and maintain the next generation of scalable AI platform for the world's best investment management technology platform. 

• Implement and manage Kubernetes clusters for deploying AI models. 

• Build platform abstractions to manage cloud-native infrastructure across AWS, GCP, or Azure environments. 

• Build and maintain automated pipelines for continuous training, testing, and deployment of machine learning models, with integrated enterprise concerns. 

• Ensure the security and compliance of the platform. 

• Troubleshoot and resolve issues related to platform performance and reliability. 

• Refine business and functional requirements and translate them into scalable technical designs. 

• Apply quality software engineering practices throughout the software development lifecycle. 

• Work with team members in a multi-office, multi-country environment. 

• Stay updated with the latest trends and technologies in AI and cloud engineering. 

Requirements: 

• B.S./M.S. degree in Computer Science, Engineering, or a related subject area. 

• 10+ years of experience in software and platform engineering. 

• Proficiency in designing and building scalable APIs and microservices. 

• Strong proficiency in Kubernetes, including Helm charts, Kustomize, and custom resource definitions (CRDs). 

• Hands-on experience with cloud platforms such as AWS, GCP, or Azure. 

• Expertise in containerization technologies (Docker, containerd). 

• Experience in CI/CD tools (Jenkins, GitHub Actions, ArgoCD). 

• Knowledge of infrastructure such as code (IaC) tools like Terraform or CloudFormation. 

• Solid understanding of networking concepts, security policies, and API gateways in cloud environments. 

• Proficiency in production-grade programming languages such as Rust and C++. 

• Decent understanding of distributed systems, cluster orchestration and management. 

• Good knowledge of data science tools (e.g PyTorch, Jax, Numpy) and programming languages such as Python. 

• Experience with monitoring tools (Prometheus, Grafana). 

• Experience working in Agile development teams with excellent collaboration skills. 

• Grit in the face of technical obstacles. 

Nice to have: 

• Building SDKs or client libraries to support API consumption. 

• Knowledge of distributed data processing frameworks (Spark, Dask). 

• Understanding of GPU orchestration and optimization in Kubernetes. 

• Familiarity with MLOps and ML model lifecycle pipelines. 

• Experience with AI model training and fine-tuning. 

• Familiarity with event-driven architecture and messaging frameworks like Kafka. 

• Experience with NoSQL datastores like Cassandra.