Senior Engineer - Senior AI Engineer

Bank of America•Charlotte, NC

1d•Onsite

About The Position

This job is responsible for defining and leading the engineering approach for complex features to deliver significant business outcomes. Key responsibilities of the job include delivering complex features and technology, enabling development efficiencies, providing technical thought leadership based on conducting multiple software implementations, and applying both depth and breadth in a number of technical competencies. Additionally, this job is accountable for end-to-end solution design and delivery. The Senior AI Engineer will design and deliver enterprise-grade AI agent solutions that automate document ingestion, orchestrate workflows, and augment decision-making across risk, finance, and governance domains. This role focuses on leveraging and integrating existing LLM platforms to build scalable, production-ready agent systems—embedding AI into engineering and product delivery workflows without developing foundational models. The role requires strong system design, agent orchestration, and pragmatic application of AI to drive measurable operational efficiency.

Requirements

5–8+ years of experience in software engineering, AI solution engineering, or applied data workflows
Proven experience building AI agents or agentic workflows using existing LLMs (task orchestration, tool use, memory, workflow chaining)
Hands-on experience with AI-assisted development tooling (e.g., GitHub Copilot, Copilot Studio, or equivalent) to accelerate engineering productivity and solution design
Strong understanding of RAG patterns, document ingestion, and unstructured data processing
Experience designing end-to-end AI workflows (prompting, retrieval, tool integration, output validation)
Proficiency in prompt engineering, grounding, and guardrails to ensure reliability and control
Strong Python engineering skills and experience building production-grade services and APIs
Experience with cloud-native architectures and integration into enterprise environments and tooling
Ability to define and measure outcomes (efficiency gains, cost reduction, adoption metrics)
Strong stakeholder engagement skills and ability to translate business problems into pragmatic AI solutions

Nice To Haves

Experience designing multi-agent or orchestrated agent systems for enterprise workflows
Experience building interactive copilots or embedded AI assistants within engineering or operational tools
Familiarity with agent evaluation techniques, human-in-the-loop validation, and iterative improvement loops
Experience applying AI to workflow automation, operational efficiency, or service delivery transformation
Knowledge of Responsible AI, governance, and enterprise risk considerations
Track record of driving AI adoption across engineering or product organizations
Experience operating in product-centric, outcome-driven delivery models

Responsibilities

Ensures that the design and engineering approach for complex features are consistent with the larger portfolio solution
Define the technology tool stack for the solution and evaluate and adapt new testing tool/framework/practices for team(s)
Enables team(s)/applications with Continuous Integration/Continuous Development (CI/CD) capabilities and engages with other technical stakeholders pertaining to efficient functioning of CI-CD pipeline
Guides and influences team(s) on design and best practices for high code performance –e.g. pairing, code reviews
Provides end-to-end delivery of complex features, including automation, for either a single team or multiple teams, at the program level
Conducts research, design prototyping and other exploration activities such as evaluating new toolsets and components for release management, CI/CD, and features
Works with stakeholders to establish high-level solution needs and with architects for technical requirements
This role focuses on leveraging and integrating existing LLM platforms to build scalable, production-ready agent systems—embedding AI into engineering and product delivery workflows without developing foundational models.
The role requires strong system design, agent orchestration, and pragmatic application of AI to drive measurable operational efficiency.