Sr. Java/AI Lead(W2 Role)

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.

3. CI/CD & DevOps Improvement

• Optimize and extend existing CI/CD pipelines (build, test, deploy) for Spring Boot microservices on AWS ECS.
• Implement automated quality gates, security scanning, dependency vulnerability checks, and performance regression tests.
• Reduce deployment cycle times and improve rollback capabilities across environments.

4. Codebase Health & Modernization

• Refactor legacy patterns, remove dead code, and improve architectural consistency across Java/Spring Boot applications.
• Improve observability by enhancing logging, tracing, and monitoring instrumentation.
• Standardize database query patterns, connection pooling, and PostgreSQL performance tuning.

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

Required Qualifications;

• 10+ years of hands-on Java development with deep expertise in Spring Boot, Spring Data, and RESTful API design.
• Proven ability to quickly understand and navigate large, unfamiliar codebases and grasp business context rapidly.
• Strong experience with PostgreSQL or any other relation or non-relation including query optimization, indexing strategies, and schema design.
• Solid working knowledge of AWS services, specifically ECS (Fargate/EC2), ECR, CloudWatch, RDS, S3, and IAM.
• Hands-on experience designing, building, and optimizing CI/CD pipelines (Jenkins, GitHub Actions, CodePipeline, or similar).
• Proficiency with Docker and container orchestration on AWS ECS.
• Familiarity with Angular front-end development (ability to read, review, and make targeted improvements).
• Strong Git workflow skills: branching strategies, code review, conflict resolution, and PR best practices.
• Excellent problem-solving ability and a self-starter mentality; able to operate independently with minimal direction.

Preferred Qualifications:

• Experience integrating AI/ML tools into developer workflows (Copilot, CodeWhisperer, LLM APIs, custom AI bots).
• Background in developer experience (DX) or platform engineering roles focused on internal tooling.
• Experience with infrastructure-as-code (Terraform, CloudFormation) and configuration management.
• Familiarity with observability stacks (Datadog, New Relic, ELK, or PrometheGrafana).
• Contributions to open-source projects or internal developer tooling initiatives.
• Knowledge of security best practices, OWASP guidelines, and automated security scanning tools.

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.