About this role
At BlackRock, technology is the foundation of our business. As a VP, AI Engineer (GenAI/ML),youālllead by example ā 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.Youālldesign and deliver enterprise-scale AI-enabled software with a focus on reliability, governance, and clean engineering practices.Ā
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 ā all while fostering a culture of excellence and continuous improvement.Ā
About Post Trade Accounting (PTA)Ā
A major strategic area within Aladdin and one of BlackRockās largest engineering investments.Ā
Responsible for the systems that ensureaccurate, scalable, and efficient accounting across global operations.Ā
Expanding intodata analytics and pipeline initiatives using Snowflake, Redis, and Kafka to manage high-volume, real-time data.Ā
Collaborates closely with Product, Operations, and other Engineering teams to deliver business-critical capabilities.Ā
Agile and collaborative environment that values technical depth, quality, and innovation.Ā
Key ResponsibilitiesĀ
Design and developproduction AI servicesthat embed GenAI and ML into enterprise workflows.Ā
BuildAI agentsthat can plan and execute multi-step tasks by calling approved tools/APIs, retrieving context,validatingoutputs, and safely handling failure cases.Ā
Implementretrieval-augmented generation (RAG)systems using vector search and hybrid retrieval to ground responses in enterprise data and documentation.Ā
Develop ML components whereappropriate (e.g., classification, ranking, anomaly detection) and integrate them with LLM-based systems for hybrid intelligence.Ā
Establish robustevaluation and quality gatesfor agents and LLM systems (golden datasets, automated tests, regression suites,monitoringfor drift/quality).Ā
Implement enterprise-gradegovernance: audit trails, provenance/citations, access control, privacy handling, and versioning (models/prompts/chains).Ā
Champion best practices for code quality, testing, automation, and performance/cost optimization.Ā
Mentor engineers to elevate technical craftsmanship, problem-solving, and design thinking.Ā
Collaborate cross-functionallyto ensure technical solutions align with product goals and business outcomes.Ā
Qualifications / CompetenciesĀ
B.S./M.S. in Computer Science, Engineering, or related discipline.Ā
8+ years of professional software engineering experience building andoperatingdistributed systems in production.Ā
3+ years delivering ML/AI systems to production with measurable outcomes and operational ownership.Ā
Demonstrable experience designing and building AI agents(agentic workflows) that:Ā
perform multi-step reasoning and task execution,Ā
use tools/functions/APIs,Ā
manage state appropriately,Ā
and include guardrails, validation, and safe fallback behavior.Ā
Strong knowledge ofagentic design patterns, such as planning/execution loops, routing/specialist agents, retrieval + tool orchestration, verification/reflection, and human-in-the-loop approvals.Ā
Hands-on experience withLLM orchestration frameworks(e.g.,LangChainor similar), including building reusable chains/agents and enforcing structured outputs.Ā
Hands-on experience withvector databases / retrieval systems(e.g., Pinecone,Weaviate, Milvus,pgvector, Elastic vector search, etc.) including indexing, chunking strategies, and hybrid search.Ā
Strong Python for AI/ML development and automation; strong backend experience in Java and/or TypeScript (APIs, services, integration patterns).Ā
Solid applied ML and evaluation fundamentals: dataset construction, precision/recall tradeoffs, calibration, and drift/quality monitoring.Ā
Enterprise mindset for security and controls: data minimization, auditability, traceability, access control, and operational resilience.Ā
Strong focus on clean architecture, maintainability, and production readiness.Ā
Excellent communication and leadership skills ā able to guide teams and influence design direction.Ā
Nice to HaveĀ
Experience with Kubernetes, Docker, and cloud-native environments (AWS/GCP).Ā
Observability experience:OpenTelemetry, Prometheus/Grafana, and AI/LLM telemetry (quality metrics, latency, cost, tool-call traces).Ā
MLOpsmaturity: model registry, reproducibility, CI/CD for ML, feature stores, and governance workflows.Ā
Responsible AI experience: red-teaming, prompt injection defenses, content safety filters, privacy-preserving patterns.Ā
Familiarity with enterprise workflow/case management patterns and building AI into operational processes.Ā
Interestin financial systems, accounting, or investment technology.Ā
Our benefits
To help you stay energized, engaged and inspired, we offer a wide range of benefits including a strong retirement plan, tuition reimbursement, comprehensive healthcare, support for working parents and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about.
Our hybrid work model
BlackRockās hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person ā aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock.
About BlackRock
blackrock