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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Engineer - RAG Database & Embeddings... - **Company:** Bank of America - **Location:** Charlotte, NC, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Agile Methodology, Artificial Intelligence, Microsoft Azure, Information Systems, Databases, Information Engineering, Data Stores, Database Design, Distributed Systems, Elasticsearch, Graph Database, PostgreSQL, Metadata, Open Source Technology, Performance Tuning, Systems Development Life Cycle, Release Management, Search Technologies, Software Engineering, Management of Software Versions, Enterprise Software Applications, Retrieval-Augmented Generation, Large Language Models, Indexer, AI Platforms, Information Technology, HuggingFace, Cosmos DB, Data Management, Api Design, Data Pipelines - **Published:** June 29, 2026 - **Apply:** https://www.juju.com/job/00000000gcltjt ## About the Role + 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 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, + Risk Management + Solution Design + Agile Practices + Analytical Thinking + Collaboration + Data Management + Solution Delivery Process ## 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., Bank of America and its affiliates consider for employment and hire qualified candidates without regard to race, religious creed, religion, color, sex, sexual orientation, genetic information, gender, gender identity, gender expression, age, national origin, ancestry, citizenship, protected veteran or disability status or any factor prohibited by law, and as such affirms in policy and practice to support and promote the concept of equal employment opportunity, in accordance with all applicable federal, state, provincial and municipal laws. The company also prohibits discrimination on other bases such as medical condition, marital status or any other factor that is irrelevant to the performance of our teammates. View your **"Know your Rights (https://www.eeoc.gov/sites/default/files/2023-06/22-088\_EEOC\_KnowYourRights6.12.pdf) "** poster. View the LA County Fair Chance Ordinance (https://dcba.lacounty.gov/wp-content/uploads/2024/08/FCOE-Official-Notice-Eng-Final-8.30.2024.pdf) . ## Related Videos - [Optimizing Discovery: PostgreSQL's Role in Transforming GetYourGuide's Search](https://www.wearedevelopers.com/videos/1647-optimizing-discovery-postgresql-s-role-in-transforming-getyourguide-s-search) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases) - [Accelerating GenAI Development: Harnessing Astra DB Vector Store and Langflow for LLM-Powered Apps](https://www.wearedevelopers.com/videos/966-accelerating-genai-development-harnessing-astra-db-vector-store-and-langflow-for-llm-powered-apps) - [Dynamic Entities in .NET: Building Low-Code Systems on Top of Entity Framework Core](https://www.wearedevelopers.com/videos/100218-dynamic-entities-in-net-building-low-code-systems-on-top-of-entity-framework-core) - [OLAP for AI Applications and why you should care](https://www.wearedevelopers.com/videos/100212-olap-for-ai-applications-and-why-you-should-care) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? 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