AI-Native Senior Engineering Manager, Full Stack

PwC•Cincinnati, OH

About The Position

We are seeking an AI-Native Fullt Stack Senior Engineering Manager to lead the modernization and cloud transformation of legacy and on-premises workloads into cloud-native AWS solutions. The ideal candidate combines deep, hands-on expertise in full-stack application development on AWS with strong architectural leadership in migrating and modernizing applications. You will define the AI-native engineering strategy for your teams, establish standards for AI tool adoption across the organization, evaluate emerging AI development platforms, and drive measurable efficiency gains. AI-assisted development is not an add-on — it is how your teams operate.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, or related field.
15+ years of experience in software engineering and architecture.
6+ years of hands-on AWS experience including modernization or migration.
Daily proficiency with AI coding assistants (GitHub Copilot, Cursor, Claude Code, Codex, or Kiro) with a track record of establishing AI-native engineering practices across teams.
Experience evaluating and adopting new AI development tools and platforms for engineering organizations.
Hands-on experience with agentic AI frameworks including LangChain, Claude Agent SDK, and Bedrock Agent SDK.
Experience designing applications with AI agent integration points and understanding how agentic AI intersects with the application layer.
Ability to measure and report AI-driven efficiency metrics to leadership.
Proficiency in front-end frameworks such as React, Angular, or Vue.js.
Strong back-end development skills with Node.js, Java, Python, or equivalent.
Experience migrating legacy applications to AWS using methodologies like lift-and-shift, re-platforming, and refactoring.
Hands-on experience with AWS services including Lambda, S3, RDS, DynamoDB, API Gateway, CloudFront, Cognito, IAM.
Strong knowledge of Infrastructure as Code (Terraform, CloudFormation, CDK).
Familiarity with containerization and orchestration tools (Docker, Kubernetes, ECS/EKS).
Strong understanding of application security, identity management, and cloud governance in AWS.
Strong communication, leadership, and stakeholder management skills.
Proven track record leading cloud migration or modernization projects.
Knowledge of Agile methodology and practices.
Prior experience with microservices architecture, serverless patterns, and event-driven design.

Nice To Haves

AWS Professional or Associate certifications.
DevOps experience (CI/CD pipelines using CodePipeline, GitHub Actions, Jenkins).
Experience with AWS migration tools (DMS, SCT, Migration Hub).
Experience working in enterprise multi-account AWS environments.

Responsibilities

Lead assessment, planning, and execution of full stack application migrations to AWS.
Analyze existing application portfolios for cloud readiness, dependencies, and modernization opportunities.
Architect end-to-end migration solutions including re-hosting, re-platforming, and refactoring of front-end, back-end, and data components.
Design AWS-native architectures using Lambda, EC2, ECS/EKS, API Gateway, RDS, DynamoDB, S3, CloudFront, Cognito, and IAM.
Develop Infrastructure as Code using Terraform, CloudFormation, or AWS CDK.
Implement CI/CD pipelines incorporating AI-powered code quality gates, automated security scanning, and AI-assisted deployment validation.
Define and drive AI-native engineering strategy across teams, establishing standards for AI tool adoption, measuring productivity gains, and reporting efficiency metrics to leadership.
Lead and mentor engineering teams on modern development frameworks (React, Angular, Vue.js) and backend technologies (Node.js, Java, Python) with AI-augmented workflows as the default.
Design applications with AI agent integration points, evaluating and architecting agentic AI solutions within modernization and migration initiatives.
Implement AWS security best practices and govern identity, compliance, and networking.
Collaborate with DevOps, Security, QA, and business teams for delivery excellence.
Use CloudWatch, X-Ray, and CloudTrail for observability and performance optimization.
Evaluate emerging AI development tools and platforms; set adoption strategy and measure ROI for the engineering organization.
Maintain architecture and migration documentation.
Use AI coding assistants (GitHub Copilot, Cursor, Claude Code, Codex, Kiro) as your default development workflow and define the AI-native engineering strategy for the organization.
Evaluate emerging AI development tools and platforms; set adoption strategy and measure ROI.
Establish organization-wide standards for AI-assisted development, code review, testing, and deployment.
Drive measurable efficiency gains through AI tool adoption and report metrics to leadership.
Architect applications with AI agent integration points, leveraging agentic AI frameworks (LangChain, Claude Agent SDK, Bedrock Agent SDK).
Mentor engineering leaders and teams on AI-native practices as the standard operating model.