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

Use this summary to align your resume positioning with the role.

Demonstrates expertise in defining and implementing Agentic AI strategies within the software development lifecycle, focusing on integration with SDLC platforms and operationalizing AI models in Azure. Proven ability to lead cross-functional teams, manage risk, and communicate effectively with stakeholders while ensuring compliance with responsible AI principles.

Highest-signal resume keywords

Agentic AI Solutions ImplementationAzure AI Foundry ExperienceGitHub Copilot ProficiencyLLMOps/MLOps KnowledgeCross-Functional Program Leadership

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Software EngineeringAI/LLM Solutions DevelopmentRAG Solutions DesignPrompt EngineeringContext EngineeringKnowledge Graph DevelopmentDataset CurationRegression TestingObservabilityData Governance

Soft Skills

Risk ManagementStakeholder CommunicationTeam Leadership

Tools & Technologies

Microsoft Copilot StudioAzureKubernetesGitHubCI/CD PipelinesIssue TrackersQuality GatesObservability PlatformsService CatalogsMetadata Management

Industry Keywords

Regulated IndustriesModel Risk ManagementResponsible AI PrinciplesSecure Software Development

Tech Stack

Tools & technologies

AzureKubernetesSDLC

About the role

Key responsibilities & impact

Define the enterprise Agentic-AI-in-SDLC strategy, operating model, and multiyear roadmap
Align Agentic AI strategy with business objectives, enterprise architecture, and developer-productivity goals
Prioritize epics and features and drive cross-portfolio execution
Architect agentic workflows using the Microsoft Agentic Framework
Integrate agentic workflows with issue trackers, repositories, CI/CD pipelines, quality and security gates, and observability platforms
Design context architecture covering 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 integrate plugins, enterprise search, and workflow actions
Operationalize models in Azure AI Foundry with lifecycle support for evaluation, monitoring, safety, and CI/CD/CT pipelines
Define ontologies and schemas for domain knowledge graphs
Integrate code metadata, service catalogs, architectural decisions, and control libraries into retrieval ecosystems
Establish data governance for retrieval sources and grounding pipelines
Establish LLMOps practices, including prompt/version management, evaluation suites, and offline/online experiments
Maintain golden datasets and benchmarks aligned with code quality, vulnerability reduction, and MTTR goals
Partner with Risk, Legal, Privacy, InfoSec, and Model Risk on responsible AI patterns and controls
Navigate governance and control processes to meet regulatory and model-risk expectations
Lead developer onboarding, enablement, and communities of practice
Track and communicate value through KPIs and OKRs
Provide progress updates and insights to executive stakeholders

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 involving tool-use orchestration, planning, and memory/state
Experience integrating Agentic AI solutions with SDLC platforms
Hands-on experience with GitHub Copilot, including organization-level policies, telemetry, and governance
Hands-on experience with Microsoft Copilot Studio, including plugins and connectors
Hands-on experience with Azure AI Foundry, including Prompt Flow, evaluation/monitoring, safety, and compliance
Deep knowledge of RAG, prompt engineering, context engineering, and vector search
Experience building knowledge graphs and integrating them into retrieval workflows
Strong knowledge of LLMOps/MLOps, including dataset curation, evaluation design, 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 Model Risk Management or similar governance is desired
Knowledge of enterprise search, graph databases, and metadata management is desired
Background with Azure, Kubernetes, and policy-as-code is desired
Exposure to evaluation techniques and A/B testing for agents is desired

Benefits

Comp & perks

Annual discretionary incentive plan eligibility
Benefits eligibility
Paid time off
Resources and support to employees
Opportunities to learn, grow, and make an impact
Inclusive workplace
Support for physical, emotional, and financial wellness
Performance recognition and rewards