About the RoleAs an AI Engineer, you'll build the intelligence layer of the platform - the AI-powered features that allow financial services users to generate data transformations from natural language, get intelligent integration suggestions, detect data quality issues, and refine outputs based on feedback. This is where the product stops feeling like a tool and starts feeling genuinely new.
This is a hands-on production role. You'll build and iterate on LLM pipelines, RAG systems, and agentic workflows using Agno; optimize AI systems for accuracy, latency, and cost in a context where correctness matters; and collaborate closely with Backend Engineers to ensure AI capabilities are reliably surfaced through the platform. You'll work under the direction of the Head of AI Engineering and have real ownership over the features you build.
ResponsibilitiesAI Feature Development- Build and iterate on AI-powered product features - transformation generation from natural language, integration configuration suggestions, data quality detection, and automated validation
- Implement LLM pipelines that are robust, observable, and production-ready - not proof-of-concepts
LLM Pipeline Engineering- Build and optimize LLM pipelines including prompt engineering, context management, and RAG systems tailored to financial services data schemas
- Evaluate and improve AI output quality continuously - build evaluation datasets and automated scoring frameworks
- Optimize for accuracy, latency, and cost across different usage patterns
Agentic Workflows- Implement multi-step agentic workflows using Agno,
- Build workflows that handle complex, multi-turn AI interactions cleanly - with proper error handling, retry logic, and human escalation paths
Platform Collaboration- Work closely with Backend Engineers to integrate AI capabilities into the platform's API and workflow layer
- Build the service interfaces that connect AI outputs to platform execution cleanly
- Participate in code reviews and maintain high standards for code quality and testability
RequirementsExperience- 5+ years software engineering; 1+ years focused on GenAI/LLM applications in production
- Has shipped AI features to real users - not just prototypes or internal demos
Technical Skills- Hands-on with LLM APIs in production - Anthropic Claude and/or OpenAI
- Built and shipped RAG systems to production (not just experimented with them)
- Experience with agentic frameworks - Agno, LangChain, LlamaIndex, or comparable
- Strong Python proficiency
- Azure or AWS cloud experience
- Vector databases - Pinecone, Weaviate, pgvector, or comparable
- Active user of AI coding assistants in daily workflow
Nice to Have- Financial services or data domain background - understanding of financial data schemas, transformation logic, or data quality requirements adds significant context to the role
- Experience with Temporal or workflow orchestration systems
- Experience fine-tuning models or working with open-source LLMs for domain-specific tasks
- Open-source AI contributions or technical writing
- Data engineering familiarity - understanding ETL/ELT patterns helps in building more effective transformation generation features
Salary RangeMA: $175,000 - $215,000 base salary + annual target bonus
NY/ NJ: $175,000 - $215,000 base salary + annual target bonus
BBH and its affiliates' compensation program includes base salary, discretionary bonuses, and profit-sharing. The anticipated base salary range(s) shown above are only for the indicated location(s) and may differ in other locations due to cost of living and labor considerations. Base salaries may vary based on factors such as skill, experience and qualification for the role. BBH's total rewards package recognizes your contributions with more than just a paycheck-providing you with benefits that enhance your experience at BBH from long-term savings, healthcare, and income protection to professional development opportunities and time off, our programs support your overall well-being.
We value diverse experiences. We value diverse experiences and transferrable skillsets. If your career hasn't followed a traditional path, includes alternative experiences, or doesn't meet every qualification or skill listed in the job description, please do go ahead and apply.
