PwC is a leading firm focused on software and product innovation, seeking an AI-Native Full Stack Senior Engineering Manager to lead the modernization and cloud transformation of legacy workloads into cloud-native AWS solutions. The role involves defining AI-native engineering strategies, mentoring teams, and driving efficiency through AI tool adoption and cloud solutions.
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