GenAI Observability & Guardrails Engineer

Bank of America

Richmond (VA)

On-site

USD 103,000 - 180,000

Full time

7 days ago

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Job summary

Bank of America is seeking an experienced engineer to own GenAI operations in an on-prem OpenShift environment. You will deploy and support vendor applications, build APIs, and integrate guardrail and observability tools across enterprise GenAI applications.

You will monitor performance, safety, and quality, and help operationalize model-based evaluation workflows. The role emphasizes leadership, collaboration, and delivering scalable AI governance capabilities.

Qualifications

Bachelor's or Master's degree in Computer Science, Data Science, MIS, related field, or equivalent experience.
10+ years of software engineering experience designing, building, and scaling production applications.
Strong Python development experience, including data and machine learning libraries.
Experience deploying and supporting containerized workloads on OpenShift or Kubernetes.
Experience taking LLM or RAG-based applications into production.
Understanding of AI risks, agent behavior, and governance frameworks.
Experience building APIs and integrating third-party platforms.
Experience with OpenAI APIs and orchestration frameworks such as LangChain.
Understanding of inference performance optimization and observability.
Experience implementing AI guardrails and model-based evaluation frameworks.
Experience working in Agile environments.
Demonstrated technical leadership and stakeholder influence.

Responsibilities

Develop, deploy, and support vendor applications in a fully on-premises OpenShift environment.
Build APIs and integrations connecting guardrail and observability tools with enterprise GenAI applications.
Implement and maintain operational monitoring for application performance, safety, latency, and quality metrics.
Develop and calibrate model-based evaluation workflows including annotation and feedback processes.
Utilize orchestration frameworks such as LangChain for guardrails, prompt management, and scoring workflows.
Create observability dashboards and alerting capabilities.
Troubleshoot production issues and improve scalability, reliability, and performance.
Support emerging agentic and multi-step AI workflows.
Lead and mentor team members while driving engineering standards and cross-functional delivery.