Senior Engineer- Agentic AI

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.

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.

We’re seeking a Senior Engineer to lead the design and implementation of Agentic AI across the SDLC. You will define the strategy, architecture, and operating model for applying Git Hub Copilot, Microsoft Copilot Studio, Azure AI Foundry, and the Microsoft Agentic Framework—enhanced by RAG, context engineering, prompt engineering, and knowledge graphs—to automate and elevate developer workflows from planning through production. You will partner with product, platform, risk, and delivery teams to drive measurable outcomes, ensure responsible use, and scale adoption across the enterprise.

• Define the enterprise Agentic-AI-in-SDLC strategy, operating model, and multiyear roadmap, aligning with business objectives, enterprise architecture, and developer-productivity goals.
• Prioritize epics and features in the backlog and drive cross-portfolio execution to accelerate value realization and adoption.

• Architect agentic workflows using the Microsoft Agentic Framework and integrate them with SDLC systems including issue trackers, repositories, CI/CD pipelines, quality and security gates, and observability platforms.
• Design context architecture—grounding data, state management, retrieval patterns, prompt templates, and caching to ensure reliable, high-quality outcomes.
• Design RAG solutions using enterprise content, vector search, and knowledge graphs, and define reference architectures and guardrails for Copilot-based coding, testing, and automation.

• Lead delivery of AI agents that automate SDLC tasks such as requirements analysis, design reviews, test generation, traceability, code-quality enforcement, documentation updates, and release readiness.
• Build Copilots with Copilot Studio, integrating plugins, enterprise search, and workflow actions, and operationalize models in Azure AI Foundry with full lifecycle support for evaluation, monitoring, safety, and CI/CD/CT pipelines for prompts and agents.

• Define ontology and schema for domain knowledge graphs and integrate code metadata, service catalogs, architectural decisions, and control libraries to create robust retrieval ecosystems.
• Implement strong data governance for retrieval sources, ensuring correct handling of sensitive data, adherence to residency and retention rules, and delivery of high-quality grounding pipelines.

• Establish LLMOps practices including prompt/version management, evaluation suites for quality, safety, bias, and hallucination, and offline/online experiments such as A/B tests.
• Maintain golden datasets and benchmarks aligned to SDLC goals like code-quality improvement, vulnerability reduction, and MTTR reduction.

• Partner with Risk, Legal, Privacy, Info Sec, and Model Risk to define responsible‑AI patterns including policy-as-code controls, auditable logs, data-boundary enforcement, content…