Innovation Senior AI Engineer

Brown Brothers Harriman•Jersey City, NJ

1d•$210,000 - $260,000

About The Position

At BBH, Partnership is more than a form of ownership—it’s our approach to business and relationships. We know that supporting your professional and personal goals is the best way to help our clients and advance our business. We take that responsibility seriously. With a 200-year legacy and a shared passion for what’s next, this is the right place to build a fulfilling career. About Us We are on a mission to rebuild how financial services firms create, manage, and execute their data integrations and transformations. Today that process stretches across multiple teams, tools, and handoffs - slow to build, expensive to maintain, and nearly impossible to change. We collapse it into a single AI-native platform that business users can operate without writing a ticket, while engineering teams retain the governance and security controls they require. We're growing our founding team now. About the Role As our Senior AI Engineer, you'll help execute the AI strategy for the platform - facilitating how AI is used to help financial services create, configure, and optimize their data integrations and transformations. The AI layer is what turns a capable platform into a genuinely new category of product. You'll work with a team of AI engineers, remain deeply hands-on technically, and work closely with the Head of Engineering to ensure the AI layer integrates cleanly with the core platform. You'll help the organization take the decisions that matter - LLM selection, agentic architecture, RAG design, evaluation frameworks, and the interaction model that defines what "AI-native" actually means when applied to financial data workflows. The challenge here is specific: AI outputs in a financial services context need to be accurate, explainable, and auditable. Generic LLM approaches are not sufficient. You'll build something that is both intelligent and trustworthy.

Requirements

3+ years AI/ML engineering; 5+ years in a product development environment
Player-coach track record - has led teams while remaining deeply hands-on
Expert Python proficiency
Deep production LLM experience - RAG pipelines, prompt engineering, agentic systems, evaluation frameworks
Production experience with agentic frameworks - Agno strongly preferred; LangChain, LlamaIndex, or comparable also considered
Workflow orchestration experience (Temporal, Prefect, or Airflow) - AI components must operate reliably within platform workflows
Azure (primary) or AWS
Vector databases and embedding systems (Pinecone, Weaviate, pgvector, or comparable)
Active daily user of AI coding assistants - this is a cultural requirement, not just a preference

Nice To Haves

Financial services, fintech, or regulated industry background - understanding of what accuracy and auditability mean in a compliance-sensitive context
MLOps and model deployment at scale
Experience fine-tuning open-source LLMs for domain-specific tasks
Publications, conference talks, or open-source AI contributions
Experience building AI features for data tools, analytics platforms, or enterprise SaaS products

Responsibilities

Execute the AI strategy - LLM selection and management, agentic application architecture, RAG system design, prompt engineering standards, and evaluation frameworks
Help design and implement the core AI capabilities: transformation generation from natural language, integration configuration assistance, data quality detection, and intelligent validation
Determine where AI adds genuine value vs. where deterministic logic is more appropriate - this judgment is critical for a financial services product
Set technical standards and foster a culture of experimentation grounded in production discipline
Partner with the Head of Engineering and Head of Design on cross-functional AI feature development
Remain deeply technical - architect and implement core AI features
Build and evolve the transformation generation engine, integration suggestion system, and intelligent validation layer
Design the AI pipeline architecture that operates reliably inside Agno-orchestrated workflows
Establish evaluation, monitoring, and continuous improvement practices for production AI systems
Build frameworks to measure AI output quality - accuracy, consistency, and user acceptance rates
Implement production monitoring and model drift detection
Define responsible AI practices appropriate for financial services - accuracy thresholds, auditability requirements, and appropriate human-in-the-loop controls
Ensure AI outputs are explainable to both technical and non-technical users