About this role
Role Overview
The Senior AI Agent Engineer brings depth of experience and sharpness of judgment to the design and delivery of production-grade AI systems at the core of a data and knowledge product business. He/She/They take ownership of architecting sophisticated multi-agent orchestrated workflows that combine GenAI and Vision AI to extract, interpret, and structure unstructured data into high-fidelity knowledge outputs that are commercialized to customers. The Senior Engineer operates with significant autonomy — making consequential architectural decisions across the GenAI-Vision AI stack, selecting the right agent orchestration patterns for each problem, and holding the line on accuracy, scalability, and reliability in customer-facing systems. He/She/They work closely with AI/ML teams, data scientists, Engineering leads, and domain experts to ensure the resulting knowledge products are not just functional but genuinely trustworthy at commercial scale. Beyond their own delivery, the Senior Engineer actively elevates the team — through design reviews, technical mentorship, and a deep investment in engineering rigor. This is a role for an engineer who combines technical craft with commercial instinct — someone who can cut through the noise of a fast-moving field, identify what actually works at scale, and build systems that hold up in the hands of paying customers.
Roles & Responsibilities
Architect, build and deliver complex multi-agent orchestrated workflows that integrate GenAI and Vision AI capabilities to extract and structure unstructured data at production scale
Lead the technical design of end-to-end pipelines — from raw document/image ingestion through extraction, interpretation, validation, and structured knowledge output
Drive the selection and integration of agentic frameworks, LLMs, vision models, and extraction tooling based on rigorous trade-off analysis (accuracy, latency, cost, maintainability)
Define and implement systematic evaluation frameworks for agent outputs — measuring extraction accuracy, completeness, and consistency at the product level
Build robust observability, logging, and monitoring frameworks for multi-agent systems running in production
Lead efforts to address core engineering risks in knowledge extraction — hallucinations, vision model failures, edge-case document formats, accuracy degradation, and latency at scale
Collaborate with AI engineering Lead (VPs) and business stakeholders to shape solution design, ensuring the AI systems built can be credibly commercialized as data/knowledge products
Mentor junior engineers (analysts), conduct rigorous code and design reviews, and contribute to team-wide standards for AI engineering
Required Skills & Experience
Technical Skills
3–5 years of software engineering experience with at least 2–3 years of focused, hands-on work in LLM-based application development and agentic AI systems
Deep expertise across multiple agentic frameworks — LangChain, LangGraph, AutoGen, CrewAI, or custom-built orchestration systems
Proven experience designing and shipping multi-agent architectures in production, including tool use, memory, planning, and output validation modules
Strong command of GenAI, and working understanding of various agentic solution approaches using LLMs, Embeddings, graph, MCP, etc.
Strong command of Vision AI and document understanding — OCR engines, layout-aware models (LayoutLM, Donut, Pix2Struct), multimodal LLMs, image parsing, and visual grounding techniques
Hands-on experience building extraction pipelines for unstructured data — PDFs, images, scanned documents, semi-structured text — and producing reliable structured outputs (JSON schemas, knowledge graphs, relational data)
Strong Python skills and proficiency with AI/ML tooling including vector databases (Pinecone, Weaviate, pgvector), embedding models, and retrieval systems
Solid grasp of LLM evaluation techniques — benchmark design, ground-truth construction, human-in-the-loop validation, and systematic prompt optimization
Experience with cloud-native deployment, containerization (Docker, Kubernetes), and scalable infrastructure for AI workloads
Working knowledge of MLOps practices, model versioning, and production observability for AI systems serving external customers
Familiarity with responsible AI practices — data provenance, output guardrails, and customer trust considerations for commercialized AI outputs
Non-Technical & Interpersonal Skills
Strong technical communication — able to articulate architectural decisions and accuracy/cost/latency trade-offs to engineering, product, and business audiences
Pragmatic problem-solver — separates hype from substance and focuses on what delivers verifiable customer value
Commercial awareness — understands that knowledge products live or die by accuracy, consistency, and customer trust
Strong documentation habits — design docs, RFCs, evaluation reports, and post-mortems
Leadership & Ownership
Proven ability to lead technical initiatives end-to-end — from problem framing and design through delivery, monitoring, and iterative improvement
Demonstrated experience mentoring engineers and raising the technical bar through code reviews, pairing, and knowledge sharing
Influences without authority — drives alignment across engineering, product, and data science through credibility and clear reasoning
Takes full ownership of production quality — including on-call, incident response, accuracy monitoring, and long-term system health of commercialized AI outputs
What This Role Offers
Technical ownership of production-grade AI systems that directly generate commercial revenue through data and knowledge products
Architectural authority over a rare and valuable stack — GenAI, Vision AI, and multi-agent orchestration combined in a commercial context
Direct influence on the engineering roadmap and technical direction of the organization’s AI-powered product portfolio
A clear growth trajectory into staff, principal, or engineering leadership roles, supported by mentorship, exposure, and stretch opportunities.
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
BlackRock’s 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 – 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
At BlackRock, we are all connected by one mission: to help more and more people experience financial well-being. Our clients, and the people they serve, are saving for retirement, paying for their children’s educations, buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress.
This mission would not be possible without our smartest investment – the one we make in our employees. It’s why we’re dedicated to creating an environment where our colleagues feel welcomed, valued and supported with networks, benefits and development opportunities to help them thrive.
For additional information on BlackRock, please visit @blackrock | Twitter: @blackrock | LinkedIn: www.linkedin.com/company/blackrock
BlackRock is proud to be an Equal Opportunity Employer. We evaluate qualified applicants without regard to age, disability, family status, gender identity, race, religion, sex, sex
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