Associate, AI Agent Engineer

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

• 35 years of software engineering experience with at least 23 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 organizations 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

BlackRocks 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 childrens 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. Its why were 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: is proud to be an Equal Opportunity Employer. We evaluate qualified applicants without regard to age disability family status gender identity race religion sex sexual orientation and other protected attributes at law.