AI Engineer (GenAI/ML), Aladdin Engineering, Vice President

At BlackRock, technology is the foundation of our business. As a VP, AI Engineer (GenAI/ML),you’lllead by example — architecting, coding, and mentoring teams to build resilient systems that applyGenerative AI and Machine Learningto improveoperational efficiency,user experience, andinsight generationacross our global post-trade operations.You’lldesign and deliver enterprise-scale AI-enabled software with a focus on reliability, governance, and clean engineering practices. 

This role is ideal for a technical leader who enjoys staying close to the code, guiding design decisions, and buildingagentic systems(tool-using AI agents) and ML services — all while fostering a culture of excellence and continuous improvement. 

About Post Trade Accounting (PTA) 

• A major strategic area within Aladdin and one of BlackRock’s largest engineering investments. 

• Responsible for the systems that ensureaccurate, scalable, and efficient accounting across global operations. 

• Expanding intodata analytics and pipeline initiatives using Snowflake, Redis, and Kafka to manage high-volume, real-time data. 

• Collaborates closely with Product, Operations, and other Engineering teams to deliver business-critical capabilities. 

• Agile and collaborative environment that values technical depth, quality, and innovation. 

Key Responsibilities 

• Design and developproduction AI servicesthat embed GenAI and ML into enterprise workflows. 

• BuildAI agentsthat can plan and execute multi-step tasks by calling approved tools/APIs, retrieving context,validatingoutputs, and safely handling failure cases. 

• Implementretrieval-augmented generation (RAG)systems using vector search and hybrid retrieval to ground responses in enterprise data and documentation. 

• Develop ML components whereappropriate (e.g., classification, ranking, anomaly detection) and integrate them with LLM-based systems for hybrid intelligence. 

• Establish robustevaluation and quality gatesfor agents and LLM systems (golden datasets, automated tests, regression suites,monitoringfor drift/quality). 

• Implement enterprise-gradegovernance: audit trails, provenance/citations, access control, privacy handling, and versioning (models/prompts/chains). 

• Champion best practices for code quality, testing, automation, and performance/cost optimization. 

• Mentor engineers to elevate technical craftsmanship, problem-solving, and design thinking. 

• Collaborate cross-functionallyto ensure technical solutions align with product goals and business outcomes. 

Qualifications / Competencies 

• B.S./M.S. in Computer Science, Engineering, or related discipline. 

• 8+ years of professional software engineering experience building andoperatingdistributed systems in production. 

• 3+ years delivering ML/AI systems to production with measurable outcomes and operational ownership. 

• Demonstrable experience designing and building AI agents(agentic workflows) that: 

• perform multi-step reasoning and task execution, 

• use tools/functions/APIs, 

• manage state appropriately, 

• and include guardrails, validation, and safe fallback behavior. 

• Strong knowledge ofagentic design patterns, such as planning/execution loops, routing/specialist agents, retrieval + tool orchestration, verification/reflection, and human-in-the-loop approvals. 

• Hands-on experience withLLM orchestration frameworks(e.g.,LangChainor similar), including building reusable chains/agents and enforcing structured outputs. 

• Hands-on experience withvector databases / retrieval systems(e.g., Pinecone,Weaviate, Milvus,pgvector, Elastic vector search, etc.) including indexing, chunking strategies, and hybrid search. 

• Strong Python for AI/ML development and automation; strong backend experience in Java and/or TypeScript (APIs, services, integration patterns). 

• Solid applied ML and evaluation fundamentals: dataset construction, precision/recall tradeoffs, calibration, and drift/quality monitoring. 

• Enterprise mindset for security and controls: data minimization, auditability, traceability, access control, and operational resilience. 

• Strong focus on clean architecture, maintainability, and production readiness. 

• Excellent communication and leadership skills — able to guide teams and influence design direction. 

Nice to Have 

• Experience with Kubernetes, Docker, and cloud-native environments (AWS/GCP). 

• Observability experience:OpenTelemetry, Prometheus/Grafana, and AI/LLM telemetry (quality metrics, latency, cost, tool-call traces). 

• MLOpsmaturity: model registry, reproducibility, CI/CD for ML, feature stores, and governance workflows. 

• Responsible AI experience: red-teaming, prompt injection defenses, content safety filters, privacy-preserving patterns. 

• Familiarity with enterprise workflow/case management patterns and building AI into operational processes. 

• Interestin financial systems, accounting, or investment technology.