AI Summary
Lead engineering efficiency and AI enablement for multiple Java application teams. Analyze and improve codebases, design and optimize database schemas, and deploy applications in AWS. Utilize AI tools to increase developer productivity and code quality.
Key Highlights
Lead engineering efficiency and AI enablement
Analyze and improve codebases
Design and optimize database schemas
Key Responsibilities
Analyze and understand existing Java/Spring Boot applications
Identify and remediate performance gaps, inefficiencies, and modernization opportunities
Design and optimize database schemas and data models
Improve CI/CD pipelines and containerized cloud deployments
Deploy and manage applications in AWS
Technical Skills Required
Benefits & Perks
Contract-to-hire role
Potential for continued work
Nice to Have
Experience integrating AI/ML tools into developer workflows
Background in developer experience (DX) or platform engineering roles focused on internal tooling
Job Description
Dice is the leading career destination for tech experts at every stage of their careers. Our client, Shree Narayani Networking Solutions LLC, is seeking the following. Apply via Dice today!
Senior Java Developer (AI Engineering)
Location - Hybrid in Charlotte, NC
Conttract
IMPORTANT - Local or relocation candidates will be considered but final round interview will be in person in Charlotte NC
This position serves as an engineering efficiency and AI enablement lead supporting multiple Java application teams. The selected candidate will embed across teams, quickly understand existing codebases, and deliver improvements that increase developer productivity, code quality, and deployment efficiency using AI-assisted development practices. Candidates should clearly demonstrate how they have used AI and automation to increase engineering velocity, reduce toil, improve pipelines, or modernize development practices.
Key Responsibilities
- Analyze and understand existing Java/Spring Boot applications (architecture, business logic, technical debt)
- Identify and remediate performance gaps, inefficiencies, and modernization opportunities
- Actively use AI tools (Copilot, Gemini, Cursor, etc.) to improve code quality, automate reviews, and increase velocity
- Design and optimize database schemas and data models (Postgres preferred)
- Improve CI/CD pipelines and containerized cloud deployments
- Deploy and manage applications in AWS (Azure/Google Cloud Platform acceptable)
Key Responsibilities:
- 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.
- 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.
- 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.
- 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.
- 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
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- 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.
- 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.
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.
- 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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Introduction
- The meeting is to discuss the Senior Java Developer opening at Srini and Sagato.
- The request is still routing through the Hartford approvals, so it''s not ready to be distributed yet.
- They are looking for a senior Java software engineer with experience in developing enterprise applications in a typical Java Stack front end.
- Spring boot or any other similar framework.
- Good at designing and developing database design and data models.
- Well versed with AWS or any other cloud to deploy and manage the end to end application.
- The candidate should be using AI heavily in product development in the SDLC phase.
- Resumes often list hundreds of technologies, but a developer cannot develop an enterprise application without specific skills.
- Front-end: Angular Typescript experience is not required, but other technologies should have working experience.
- Database: Postgres is preferred, but experience with SQL Server or Oracle is acceptable because the concepts are the same.
- Cloud: AWS is listed, but Azure or Google Cloud experience is also acceptable because the constructs are the same.
- CI/CD: Three popular tools are listed, but experience with other tools like Harness or Deploy is acceptable because the concepts are the same.
- Containerization: Experience with AWS ECS is listed, but Kubernetes or OpenShift experience is acceptable.
- AI Tooling: GitHub Copilot, Cursor, Cloudboard, Gemini, or other AI tools are acceptable.
- The role is cross-cutting, involving four to five Java applications.
- Responsibilities include:
- Understanding the code base of each application.
- Understanding the business context, application architecture, and code structure (discovery phase).
- Identifying and fixing gaps using AI.
- Increasing the scale of velocity by using AI to solve discovered problems.
- The role is dedicated to increasing engineering efficiency.
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- The interview process will involve multiple rounds.
- Core challenges, problems, and whiteboard exercises will be included.
- Candidates will be asked to demonstrate their design thinking using PowerPoint or any other tool to address interviewer questions.
- If the candidate is in Charlotte, there will be an in-person interview.
- The speaker confirms flexibility in terms of prior experience with specific AI tools like GitHub Copilot.
- As long as candidates have AI experience with any industry-standard tool, that is totally fine.
- The AI experience should involve AI-powered utilities like code review bots.
- The person needs to be hybrid in Charlotte.
- Charlotte is the preference, Hartford is the second preference if they don''t find a candidate in Charlotte.
- It''s a potential contract-to-hire role.
- The work may continue, but a decision will be made within six months regarding contract-to-hire.
- Preference will be given to a strong Java developer with insurance experience.
- Not expecting equal proficiency on Angular.
- The candidate should be a Java person who also knows DevOps.
- The candidate will be guided and provided with the right tools and knowledge to work on the technology.
- All teams are well-versed and advanced in AI.
- The team is good at leveraging AI to solve software development or business problems.
Risabh Pal
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