Principal Engineer - RAG Database & Embeddings Architect

Bank of America•Charlotte, NC

3d•Onsite

About The Position

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.

Requirements

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.
Bachelor’s degree in Computer Science, Engineering, Information Systems, Applied Mathematics, or a related technical field

Nice To Haves

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

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.