Senior Engineer – Agentic AI

Bank of America

. Define the enterprise Agentic-AI-in-SDLC strategy, operating model, and multiyear roadmap .

Posted 10/16/2026full-timeUnited StatesSenior💰 $122,000 - $200,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in defining and implementing Agentic AI strategies within the software development lifecycle, focusing on architecting workflows, integrating AI solutions, and establishing governance frameworks. Proficient in leading cross-functional teams and managing risk while ensuring compliance with responsible AI principles.

Highest-signal resume keywords

10+ Years In Software Engineering/Architecture2+ Years Leading AI/LLM SolutionsHands-On Experience With GitHub CopilotDeep Knowledge Of RAG And Prompt EngineeringExpertise With SDLC Toolchains

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Agentic AI Solutions ImplementationArchitecting WorkflowsKnowledge Graphs DevelopmentLLMOps/MLOps PracticesData GovernanceContext EngineeringVector SearchPrompt EngineeringIntegration With SDLC PlatformsEvaluation Techniques

Soft Skills

Cross-Functional LeadershipRisk ManagementExecutive Communication

Tools & Technologies

Microsoft Copilot StudioAzure AI FoundryGitHubCI/CD PipelinesContainer OrchestrationKubernetesArtifact RepositoriesService CatalogsTesting FrameworksPolicy-As-Code

Industry Keywords

Responsible AI PrinciplesData PrivacySecure Software DevelopmentModel Risk ManagementRegulated Industries

Tech Stack

Tools & technologies

AzureCloudKubernetesSDLC

About the role

Key responsibilities & impact

Define the enterprise Agentic-AI-in-SDLC strategy, operating model, and multiyear roadmap
Prioritize epics and features in the backlog and drive cross-portfolio execution
Architect agentic workflows using the Microsoft Agentic Framework and integrate them with issue trackers, repositories, CI/CD pipelines, quality and security gates, and observability platforms
Design context architecture, including grounding data, state management, retrieval patterns, prompt templates, and caching
Design RAG solutions using enterprise content, vector search, and knowledge graphs
Define reference architectures and guardrails for Copilot-based coding, testing, and automation
Lead delivery of AI agents automating requirements analysis, design reviews, test generation, traceability, code-quality enforcement, documentation updates, and release readiness
Build Copilots with Copilot Studio and operationalize models in Azure AI Foundry
Define ontologies and schemas for domain knowledge graphs and integrate code metadata, service catalogs, architectural decisions, and control libraries
Establish data governance for retrieval sources and grounding pipelines
Establish LLMOps practices, evaluation suites, experiments, golden datasets, and benchmarks
Partner with Risk, Legal, Privacy, InfoSec, and Model Risk on responsible-AI patterns and governance
Lead developer onboarding, enablement, and communities of practice
Track and communicate value through KPIs and OKRs and provide executive updates

Requirements

What you’ll need

10+ years in software engineering/architecture
2+ years leading AI/LLM or intelligent automation solutions in production, ideally at enterprise scale
Proven experience implementing Agentic AI solutions, including tool-use orchestration, planning, and memory/state
Experience integrating Agentic AI solutions with SDLC platforms
Hands-on experience with GitHub Copilot, Microsoft Copilot Studio, and Azure AI Foundry
Deep knowledge of RAG, prompt engineering, context engineering, and vector search
Experience building knowledge graphs and integrating them into retrieval workflows
Strong grasp of LLMOps/MLOps, including dataset curation, evaluation design, prompt/agent regression testing, observability, safety/guardrails, and rollout strategies
Expertise with SDLC toolchains, including GitHub, CI/CD, IaC, testing frameworks, SAST/DAST, artifact repositories, service catalogs, and runbooks
Ability to lead cross-functional programs, manage risk, and communicate with executive and engineering stakeholders
Familiarity with responsible AI principles, data privacy, and secure software development practices
Experience in regulated industries and with Model Risk Management or similar governance
Knowledge of enterprise search, graph databases, and metadata management
Background with cloud platforms, preferably Azure, container orchestration such as Kubernetes, and policy-as-code
Exposure to evaluation techniques and A/B testing for agents

Benefits

Comp & perks

Annual discretionary incentive plan eligibility
Industry-leading benefits
Paid time off
Resources and support for employees
In-office flexibility based on role-specific considerations