Principal Engineer Data Ingestion & AI Pipeline

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 Data Ingestion & AI Pipeline Principal Engineer is responsible for designing and leading the engineering of scalable ingestion pipelines that prepare enterprise data for RAG and AI workloads. This role focuses on extracting transforming enriching validating chunking classifying and loading structured and unstructured data into downstream RAG databases.

This engineer ensures that source data becomes clean traceable secure current and optimized for retrieval and LLM consumption.

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 lead enterprise data ingestion pipelines for structured semi-structured and unstructured data.
• Build scalable ingestion patterns for sources such as databases APIs documents PDFs SharePoint file shares data lakes wikis ticketing systems code repositories emails and application logs.
• Define transformation patterns for document parsing text extraction normalization deduplication enrichment chunking classification and metadata generation.
• Establish ingestion standards for source lineage data freshness versioning access controls and auditability.
• Implement pipeline orchestration monitoring retry logic error handling and operational dashboards.
• Design incremental ingestion change detection delta processing and reprocessing strategies.
• Partner with RAG database engineers to ensure ingested data is optimized for embedding indexing and retrieval.
• Partner with context engineers to ensure data is structured and enriched in ways that support effective LLM reasoning.
• Evaluate and implement OCR document intelligence parsing entity extraction content classification and data quality capabilities.
• Define data quality checks for completeness accuracy duplication stale content unsupported formats and sensitive information.
• Ensure ingestion pipelines comply with security privacy retention and regulatory requirements.
• Provide technical leadership across data engineering AI platform cloud and application 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.
• Extensive experience designing and operating production-grade data pipelines.
• Strong understanding of ETL/ELT streaming batch processing orchestration and data quality engineering.
• Experience ingesting and processing structured and unstructured enterprise content.
• Hands-on experience with cloud data platforms and pipeline technologies such as Azure Data Factory Synapse Databricks Fabric Spark Kafka Event Hubs Airflow dbt or equivalent.
• Experience with document processing OCR schema inference parsing content extraction and metadata enrichment.
• Understanding of RAG architectures embeddings vector databases and LLM-oriented data preparation.
• Strong knowledge of data governance lineage access control propagation and enterprise security patterns.
• Proven ability to design reusable engineering frameworks and standards.
• Proven ability to lead architecture across multiple engineering teams.
• Strong written and verbal communication skills.
• Experience operating systems in regulated security-conscious or enterprise-scale environments.
• Bachelors degree in Computer Science Engineering Information Systems Applied Mathematics or a related technical field

Desired Qualifications:

• Experience with Azure AI Document Intelligence Microsoft Graph SharePoint ingestion Purview or enterprise content management systems.
• Experience with large-scale data lakehouse architectures.
• Experience with semantic chunking entity extraction taxonomy generation or knowledge graph creation.
• Experience with sensitive data detection PII handling DLP and compliance-aware ingestion.
• Reliable ingestion pipelines that continuously feed high-quality enterprise knowledge into RAG systems.
• Reduced manual data preparation and improved automation of content onboarding.
• Strong lineage governance and access-control-aware ingestion patterns.
• Faster time-to-ingest for new enterprise data sources.
• Improved downstream retrieval and LLM response quality through better data preparation.
• 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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