Principal Engineer RAG Database & Embeddings Architect

Job Description:

At Bank of America we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients teammates communities and shareholders every day.

Being a Great Place to Work and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed build a career and contribute to our shared success. This includes attracting and developing exceptional talent recognizing and rewarding performance and supporting our teammates physical emotional and financial wellness through affordable competitive and flexible benefits.

We value the unique perspectives individuals bring from all backgrounds and career paths - whether shaped by military service community college education or a wide range of work and life experiences. These journeys foster resilience leadership and innovation strengthening our workforce and positively impact the communities we serve.

Bank of America is committed to an in-office culture that supports collaboration engagement and career development. Our approach includes clear in-office expectations while providing an appropriate level of flexibility based on role-specific responsibilities and business needs.

At Bank of America you can build a successful career with opportunities to learn grow and make an impact. Join us!

Job Description:
This job is responsible for defining and leading the engineering approach for solutions at the program or portfolio level to deliver significant business outcomes. Key responsibilities include continuously improving the design quality and reuse of the solution and delivering technology enablers that improve development efficiencies for the solution. Job expectations include familiarity with at least one area of engineering acting as a go to reference across the organization and applying knowledge to improve technical competencies through recruitment and development activities.

Developer Experience (DevEx) provides enterprise technical standards and common technical services platforms and tools that are leveraged by delivery teams across all lines of business. Within the SDLC Software Delivery Lifecycle program this role leads portfolio product delivery strategy and execution for enterprise software delivery capabilities ensuring the right investments operating model governance and prioritization are in place to improve how internal technical users build test and deliver software at scale.

The RAG Database & Embeddings Architect is responsible for designing building and governing the vector database and retrieval architecture that powers enterprise Retrieval-Augmented Generation systems. This role focuses on embeddings vectorization strategies semantic search indexing metadata modeling hybrid retrieval relevance tuning and performance optimization.

This engineer will define how enterprise knowledge is represented stored retrieved ranked and refreshed for use by LLM-powered applications.

Responsibilities:

• Develops the engineering approach for the entire program/portfolio solution and works with Architecture to develop/analyze/deliver the implementation of technical enablers
• Leads the planning definition and design of the complex features which span multiple teams and explore solution alternatives
• Creates ideas on designing complex technology and solution development approaches
• Leads the technical oversight for teams in solution development including design reviews and code within own domain
• Defines the technology tool stack for the solution within ranged of internally approved and supported technologies
• Explores state-of-the-art technologies to improve development efficiencies quality of test/QA coverage and release management
• Leads and is responsible for the end-to-end test strategy/creation/adherence and the integration between teams for a program/portfolio solution
• Improve the experience for our developers making it easier to deliver industry-leading solutions while managing work efficiently and with the right controls
• Advance our technology platforms through innovation
• Reduce risk and improve quality across our technology portfolio by aligning to a single enterprise architecture strategy and delivering governance that enables consistency integration and automation
• Design and own the architecture for enterprise RAG data stores including vector databases document stores metadata stores and hybrid search layers.
• Define embedding strategies across structured semi-structured and unstructured content.
• Evaluate and select embedding models based on accuracy latency cost domain fit multilingual needs and operational constraints.
• Design vectorization workflows including chunking embedding generation indexing versioning and re-embedding lifecycle management.
• Implement semantic keyword metadata-filtered and hybrid retrieval patterns.
• Optimize retrieval quality using similarity metrics reranking query expansion metadata boosting and relevance feedback.
• Establish standards for vector schema design namespace strategy document lineage source attribution and access-control-aware retrieval.
• Partner with data pipeline engineers to ensure ingestion processes produce high-quality retrievable content.
• Partner with context engineers to tune retrieval outputs for downstream LLM consumption.
• Define observability for retrieval quality including recall precision latency cost freshness and hallucination risk indicators.
• Lead technical evaluation of vector database platforms and retrieval frameworks.
• Provide engineering leadership design reviews mentoring and architectural guidance across AI platform teams.
• Serve as a senior technical authority for enterprise AI platform engineering.
• Own architecture decisions that impact multiple teams systems or domains.
• Create reusable patterns reference architectures standards and engineering guardrails.
• Mentor senior engineers and influence technical direction without requiring direct reporting authority.
• Balance innovation with operational reliability security compliance scalability and cost management.
• Communicate complex AI and data engineering concepts clearly to engineering product risk security and executive stakeholders.

Required Qualification:

• 10 years of software engineering data engineering platform engineering or AI engineering experience.
• 5 years designing large-scale enterprise systems.
• 2 years working with LLM RAG vector search semantic search or AI platform capabilities.
• Experience operating systems in regulated security-conscious or enterprise-scale environments.
• Extensive experience designing production-grade search indexing or database systems.
• Strong understanding of vector databases embeddings similarity search approximate nearest neighbor algorithms and retrieval optimization.
• Experience with RAG architectures and enterprise-scale knowledge retrieval.
• Hands-on experience with platforms such as Azure AI Search Cosmos DB vector search Pinecone Weaviate Milvus OpenSearch Elasticsearch PostgreSQL/pgvector or equivalent.
• Experience with embedding models from providers such as OpenAI Azure OpenAI Cohere Hugging Face or open-source model ecosystems.
• Strong background in distributed systems database design API design and performance tuning.
• Experience designing metadata models access control filtering document provenance and auditability.
• Ability to define engineering patterns and standards used across multiple teams.
• Proven ability to lead architecture across multiple engineering teams.
• Strong written and verbal communication skills.
• Bachelors degree in Computer Science Engineering Information Systems Applied Mathematics or a related technical field

Desired Qualifications:

• Experience with hybrid retrieval reranking models knowledge graphs or entity-aware retrieval.
• Experience supporting regulated or enterprise environments with security compliance lineage and privacy requirements.
• Experience with LLM evaluation retrieval evaluation and automated relevance testing.
• Familiarity with model drift embedding drift and re-indexing strategies.
• High-quality retrieval architecture that improves LLM answer accuracy and reduces hallucinations.
• Scalable vector database strategy supporting multiple enterprise domains.
• Clear standards for embeddings metadata indexing and retrieval evaluation.
• Measurable improvements in retrieval precision recall latency and cost efficiency.
• Enterprise architecture
• Distributed systems design
• AI platform engineering
• Data governance and security
• Cloud-native engineering
• Observability and operational excellence
• Technical strategy and roadmap development
• Cross-functional influence
• Vendor and platform evaluation
• Production support and continuous improvement

Skills:

• Automation
• Influence
• Result Orientation
• Stakeholder Management
• Technical Strategy Development
• Application Development
• Architecture
• Business Acumen
• Risk Management
• Solution Design
• Agile Practices
• Analytical Thinking
• Collaboration
• Data Management
• Solution Delivery Process

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