This is a 6 month contract-to-hire and needs to meet Client full-time conversion policies. Those dependent on a work permit sponsor now or anytime in the future (ie H1B, OPT, CPT, etc) do not meet Client requirements for this opening.
**MUST BE HYBRID in Charlotte, NC
**MUST BE W2; No Corp-to-Corp**
We are looking for a Senior Java Developer who will serve as a dedicated engineering
efficiency champion across multiple application teams. Unlike typical feature developers, this
role is singularly focused on identifying, building, and shipping improvements that raise the
productivity, code quality, and operational maturity of the entire engineering organization with
a strong emphasis on leveraging AI tooling.
You will embed with different application teams, understand their codebases and business
contexts rapidly, and deliver pull requests that introduce automation, reduce toil, improve CI/CD
pipelines, and integrate AI-assisted development practices
Key Responsibilities
1. Cross-Team Engineering Efficiency
Rotate across multiple application teams to identify efficiency bottlenecks, technical
debt, and automation opportunities.
Deliver production-ready pull requests that improve build times, test coverage,
deployment reliability, and developer experience.
Establish reusable patterns, shared libraries, and internal tooling that all teams can
adopt.
2. AI-Powered Development Practices
Evaluate and integrate AI coding assistants (e.g., GitHub Copilot, custom LLM-based
tools) into the team s daily workflow.
Build internal AI-powered utilities such as automated code review bots, intelligent test
generators, documentation generators, and PR summarizers.
Champion AI-augmented development practices and train teams on effective prompt
engineering and AI-assisted coding techniques.
Identify high-ROI areas where AI can accelerate development cycles, reduce repetitive
work, or improve code quality
Technology Stack
Layer Technologies
Backend Java 17+, Spring Boot, Spring Data JPA, Spring Security, REST APIs
Frontend Angular (TypeScript)
Database PostgreSQL
Cloud & Infra AWS (ECS, ECR, CloudWatch, S3, RDS, IAM, VPC)
CI/CD Jenkins / GitHub Actions / AWS CodePipeline (or equivalent)
Containerization Docker, AWS ECS (Fargate or EC2 launch type)
AI Tooling GitHub Copilot, LLM APIs, custom AI integrations
What Success Looks Like
Timeframe Expected Outcomes
First 30 Days Onboarded across primary applications; completed codebase audits;
identified top 10 efficiency improvement opportunities; first PRs merged.
60 Days Delivered measurable CI/CD improvements; introduced at least one AI powered tool or workflow adopted by a team; established efficiency backlog.
90 Days Demonstrated quantifiable productivity gains (e.g., reduced build times,
increased automated test coverage, faster PR turnaround); AI integration
roadmap published.
Ongoing Continuous stream of high-impact PRs across teams; recognized as the go-to
resource for engineering efficiency and AI-assisted development practices.
Key Attributes
Fast Learner Can absorb a new codebase and its business context within days, not
weeks.
High Agency Self-directed; identifies problems and ships solutions without waiting for
instructions.
Collaborative Works diplomatically across teams, earns trust quickly, and influences
without authority.
Pragmatic Knows the difference between perfect and effective; optimizes for impact
over elegance.
AI-Curious Genuinely excited about using AI to transform how software teams work.
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