Vice President, AI Platform Engineering

About the Role

BlackRock is one of the world’s leading providers of investment, advisory, and risk management solutions, powered byAladdin, our integrated investment and risk management technology platform. Aladdin unifies data, analytics, and workflows across public and private markets, enabling scale, insights, and transformation for BlackRock and our clients.

As part ofAladdin Engineering, you will join theAI Platform Engineering team, which is building the next-generation AI infrastructure and services that power Aladdin and other firm-wide applications. This team sits at the intersection ofbackendsystems, AI engineering,AIinfrastructure, and platform engineering,enabling advanced AI capabilities at scale.

We are looking for asenior individual contributorwho thrives on solving complex engineering challenges, shaping AI software strategy, and building robust, scalable platforms. You will work alongside some of the brightest minds in financial technology, influencing the AI ecosystem across the firm.

WhatYou’llDo

• Architect and buildscalable AI platform components that support model training, tuning, inference, and lifecycle management.

• Design and implementcloud-native infrastructure abstractions across AWS, GCP, and Azure for AI workloads.

• Develop andoptimizeKubernetes-based orchestration for AI models, including GPU scheduling and resource management.

• Build automated pipelinesfor continuous training, testing, and deployment of ML models with enterprise-grade security and compliance.

• Collaborate cross-functionalto translate business and functional requirements into resilient technical designs.

• Ensure reliability and performanceof distributed systems through proactive monitoring, troubleshooting, and optimization.

• Stay ahead of trendsin AI engineering,MLOps, and cloud-native technologies to drive innovation.

WhatWe’reLooking For

• Education: B.S./M.S. in Computer Science, Engineering, or related field.

• Experience: 6+ years in software/platform engineering with a focus on distributed systems and cloud-native architectures.

• Software Architecture & Design:

• Proven ability to designscalable, fault-tolerant, and modular systemsfor high-performance AI and backend platforms.

• Expertiseindistributed systems architecture, including service decomposition, API design, and data flow optimization.

• Strong understanding ofdesign patterns, domain-driven design (DDD), and best practices for building extensible platforms.

• Experience creatingplatform abstractionsand reusable components for multi-tenant, cloud-native environments.

• Cloud-Native Engineering:

• DeepproficiencyinKubernetes(Helm,Kustomize, CRDs) and containerization (Docker,containerd).

• Hands-on experience withAWS, GCP, Azure, andIaCtools (Terraform, CloudFormation).

• Programming Expertise:

• Production-grade coding inRust, C++,etc.and scripting inPythonfor AI workflows.

• Familiarity with ML frameworks (PyTorch, JAX, NumPy) andMLOpsconcepts.

• Platform Reliability & Security:

• Strong grasp ofnetworking, security policies, and API gatewaysin distributed environments.

• Experience with observability tools (Prometheus, Grafana) and performance tuning.

• Mindset: Strong problem-solving skills, adaptability, and resilience in the face of technical challenges.

Nice to Have

• Architectural Extensions: Experience designing SDKs or client libraries that enable seamless API integration and developer adoption.

• GPU-Oriented Engineering: Deep understanding of GPU orchestration, resource scheduling, and performance optimization in Kubernetes clusters.

• AI EngineeringStack:Exposure to model inference optimization, distributed training strategies, parameter tuning, and integration of ML frameworks (PyTorch, JAX,TensorRT) into production-grade systems.

• AdvancedMLOps: Practical knowledgeofend-to-end ML lifecycle management, including automated training, fine-tuning, and deployment pipelines for large models.

• Event-Driven & Streaming Systems:Expertisein building real-time data pipelines usingKafkaor similar messaging frameworks.

• Performance Profiling & Optimization: Ability to diagnose and optimize system bottlenecks acrosscompute, storage, and network layers.

• Security & Compliance in AI Systems:Understanding ofsecure model deployment practices and compliance frameworks for regulated industries.

Why Join Us?

You’llbe part of a team that is defining the future of AI in financial technology. At BlackRock, we value diversity, curiosity, and technical excellence. This is an opportunity to grow as a technologist while working on systems thatimpactglobal markets.