AI Engineer - Guardrails and Observability

: 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

. We value the unique perspectives individuals bring from all backgrounds and career paths - whether shaped by military service, community college

, 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

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

: This role centers on engineering and operating third-party applications end to end within an on-premises OpenShift platform. The position supports GenAI operations, including guardrails and observability for LLM and RAG-based applications across the enterprise. The engineer will deploy and support vendor platforms such as Galileo and LangSmith, monitor AI application quality and safety, maintain model evaluation workflows, and provide operational support in a fast-paced environment. The domain is GenAI operations: the applications you run provide guardrails and observability for LLM- and RAG-powered systems across the enterprise. You will work with these vendor tools to monitor application quality, safety, and performance—tracking metrics such as attribution, grounding, prompt-injection detection, tone, bias, and PII handling—and surface them through dashboards and alerting. Where the platform uses model-based evaluation (LLMs-as-Judges and specialized/small language models), you will help operationalize, calibrate, and maintain those workflows. Because the AI landscape changes quickly, the ideal engineer learns new tools and environments fast, brings a pragmatic build-and-operate mindset, and can lead cross-functional work to move initiatives forward.