Senior AI Engineer - GenAI/ML, Aladdin Engineering - Vice President

BlackRock • New York, NY

11d • $162,000 - $215,000 • Hybrid

About The Position

About this Role: At BlackRock, technology is the foundation of our business. As an AI Engineer (GenAI/ML), you’ll lead by example — architecting, coding, and mentoring teams to build resilient systems that apply Generative AI and Machine Learning to improve operational efficiency, user experience, and insight generation across our global post-trade operations. You’ll design 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 building agentic 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 ensure accurate, scalable, and efficient accounting across global operations. Expanding into data 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.

Requirements

• B.S./M.S. in Computer Science, Engineering, or related discipline.
• 8+ years of professional software engineering experience building and operating distributed 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 of agentic design patterns, such as planning/execution loops, routing/specialist agents, retrieval + tool orchestration, verification/reflection, and human-in-the-loop approvals.
• Hands-on experience with LLM orchestration frameworks (e.g., LangChain or similar), including building reusable chains/agents and enforcing structured outputs.
• Hands-on experience with vector 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 Haves

• 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).
• MLOps maturity: 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.
• Interest in financial systems, accounting, or investment technology.

Responsibilities

• Design and develop production AI services that embed GenAI and ML into enterprise workflows.
• Build AI agents that can plan and execute multi-step tasks by calling approved tools/APIs, retrieving context, validating outputs, and safely handling failure cases.
• Implement retrieval-augmented generation (RAG) systems using vector search and hybrid retrieval to ground responses in enterprise data and documentation.
• Develop ML components where appropriate (e.g., classification, ranking, anomaly detection) and integrate them with LLM-based systems for hybrid intelligence.
• Establish robust evaluation and quality gates for agents and LLM systems (golden datasets, automated tests, regression suites, monitoring for drift/quality).
• Implement enterprise-grade governance: 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-functionally to ensure technical solutions align with product goals and business outcomes.

Benefits

• employees are eligible for an annual discretionary bonus, and benefits including healthcare, leave benefits, and retirement benefits
• strong retirement plan
• tuition reimbursement
• comprehensive healthcare
• support for working parents
• Flexible Time Off (FTO)

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level