Data and AI Engineer

Bank of America•Pennington, AL

1d•$104,000 - $157,700•Onsite

About The Position

At Bank of America, the Data and AI Engineer drives efforts to develop and deliver complex data solutions that support technology and business goals. The role leads code design and delivery for integrating, cleaning, transforming, and controlling data in operational and analytical systems, and works with vendors, stakeholders, Product teams, and Software Engineering teams to implement requirements, analyze performance, and troubleshoot system engineering issues. The engineer designs, develops, optimizes, and supports enterprise-scale data platforms, with a foundation in Oracle and SQL Server database development, data engineering, and ETL. The role also spans Python, backend APIs, retrieval and search technologies, and AI integration. The engineer collaborates with Business Analysts, UI teams, and Production Support teams, and uses technologies including Oracle, SQL Server, Python, SSIS, Autosys, and AI-assisted development tools such as Microsoft Copilot and GitHub Copilot. The position includes technical leadership across requirements refinement, development, testing, data management standards, project delivery, and mentoring. It also involves building and supporting data pipelines and AI-enabled solutions, including retrieval-augmented generation and vector or hybrid search, and helping productionize AI capabilities through testing, observability, security, governance, deployment, and operational support. The role is based at the listed Pennington, New Jersey location and is designated for first shift, with a 40-hour workweek.

Requirements

At least 7 years of experience in Oracle and SQL Server database development.
Strong expertise in SQL, PL/SQL, and T-SQL.
Experience with database architecture and logical and physical data modeling.
Hands-on experience with SSIS and ETL development.
Strong Python development skills, including automation and backend/API development.
Hands-on understanding of LLMs, RAG, embeddings, vector stores and search, and hybrid search.
Understanding of how embeddings are generated, stored, indexed, and retrieved for AI-enabled data solutions.
Working knowledge of AI productionization practices, including testing, observability, security, governance, and operational readiness.
Experience with Autosys job scheduling and workflow management.
Expertise in database design, schema creation, stored procedures, functions, views, and indexing.
Strong experience in SQL query optimization, performance tuning, and troubleshooting.
Experience supporting production deployments and application releases.
Excellent communication, problem-solving, and documentation skills.
Experience working in Agile/Scrum environments.

Nice To Haves

Experience with FastAPI or a similar framework.
Exposure to agentic workflows and frameworks such as LangChain and LangGraph.
Experience with Elasticsearch or another platform supporting vector and hybrid search patterns.
Experience with Apache Airflow for workflow orchestration and data pipeline scheduling.
Knowledge of MongoDB or other NoSQL databases.
Experience with CI/CD tools such as Jenkins.
Experience with Git, Bitbucket, JIRA, and Confluence.
Experience with cloud-based data platforms and modern data engineering practices.
Experience in financial services or large enterprise environments.

Responsibilities

Lead story refinement and deliver requirements through the delivery lifecycle; assist team members in resolving technical complexities.
Code complex solutions to integrate, clean, transform, and control data; build data transformation processes, structures, metadata, data quality controls, dependency management, and workload management.
Assemble complex data sets and communicate information required for deployment.
Lead documentation of system requirements, collaborate with development teams to understand data requirements and feasibility, and use architectural components to develop client requirements.
Lead testing teams in developing test plans; contribute to integration, regression, and performance test suites; analyze test reports, identify test issues and errors, and lead triage of underlying causes.
Work with technology partners and stakeholders to close gaps in adherence to data management standards; identify and communicate solutions to complex problems.
Lead complex technology projects to support on-time delivery and compliance with release processes and risk management.
Define and build data pipelines to enable data-informed decision-making.
Mentor Data Engineers, support their continued development, and monitor key performance indicators and internal controls.
Design, develop, and maintain Oracle and SQL Server database solutions.
Create and optimize database objects, including tables, indexes, views, stored procedures, functions, and triggers.
Develop complex SQL, PL/SQL, and T-SQL queries for reporting, analytics, and operational workloads.
Design and implement ETL processes using SSIS, Python, and related technologies.
Build Python-based backend services and APIs to support data and AI integrations.
Design and integrate retrieval solutions using LLMs, RAG, embeddings, vector stores, vector search, and hybrid search.
Work with how embeddings are generated, stored, indexed, and retrieved across platforms such as Elasticsearch or similar search technologies.
Contribute to agentic workflows and orchestration patterns using frameworks such as LangChain and LangGraph, where applicable.
Help productionize AI solutions through testing, observability, security, governance, deployment, and operational support practices.
Automate and schedule data processing jobs using Autosys and other workflow orchestration tools.

Benefits

Industry-leading benefits.
Access to paid time off.
Resources and support for employees.
Eligibility to participate in the annual discretionary incentive plan; awards are based on individual performance and behaviors, line-of-business or group contributions, and company success.

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