Job description

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 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. 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. Required Qualifications: • 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 Desired Qualifications: • 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 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 Shift: 1st shift (United States of America) Hours Per Week: 40

Requirements

Must-haves

At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!
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
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)

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

Application prep

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Keywords to mirror

aillmci/cdpythonagile

Platform: workday
Effort: long

Benefits

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