Java Lead Engineer

Role : Lead Engineer

Location :Bethesda , MD

Hybrid 2-3 days

Client :Hospitality Client

The Lead Engineer will lead the replat forming of a mission-critical API platform from a legacy framework to Java Spring Boot WebFlux using AI-assisted development. The platform serves customer-facing web and mobile experiences and high-impact internal applications, so reliability, security and backward compatibility are non-negotiable.The role also helps establish AI-assisted development as a repeatable practice. You will lead a team delivering with Cursor (or an equivalent AI coding tool) under a structured AI development lifecycle (AI-DLC or equivalent), and turn what works into patterns, guardrails and metrics other teams can adopt.

Technical leadership and migration

• Own the technical design and delivery of the replatforming effort: REST and SOAP APIs migrated from a legacy framework to Spring Boot WebFlux with contract and behavioral parity.
• Define the target reference architecture: reactive patterns, error handling, resiliency (timeouts, retries, circuit breakers), observability and security for identity and PII data.
• Plan and run a low-risk cutover strategy (strangler pattern, parallel run, traffic shadowing, feature flags, staged rollout) with no disruption to consuming applications.
• Partner with integration owners across the architecture to manage API contracts, dependencies and deprecation.
• Operationalize AI-DLC or an equivalent AI development lifecycle across the team: AI-supported inception (requirements, legacy code analysis, API inventory), construction (code generation, test generation, refactoring) and operations (deployment, monitoring, runbooks), with human approval at each gate.
• Build and maintain the team's Cursor setup: project rules, prompt and context libraries, reusable templates, and agent workflows for repeatable legacy-to-target conversion patterns.
• Use AI to accelerate legacy comprehension: documenting existing application behavior, generating characterization tests and contract tests before code is migrated.
• Coach engineers on effective AI pairing: when to trust, verify, or reject AI output.
• Implement AI guardrails: approved tools and models, data handling rules (no customer data or secrets in prompts), secure coding standards, and mandatory human review of AI-generated code.
• Enforce quality gates in CI/CD: static analysis, SAST/DAST, dependency and license scanning, test coverage thresholds, and performance baselines.
• Maintain traceability of AI-assisted changes for audit and compliance needs.
• Define and report pilot metrics: cycle time, throughput, defect escape rate, rework, test coverage, cost per API migrated, and developer experience.
• Produce the playbook, reference implementations and lessons learned that other teams will use to adopt AI-assisted development.
• Present progress and findings to engineering leadership and architecture governance forums.

Required qualifications

• 12+ years of software engineering experience, including 3+ years leading engineering teams or technical workstreams.
• Expert-level Java: concurrency, performance tuning, and modern language features.
• Deep hands-on Spring Boot experience, including Spring WebFlux and Project Reactor (Mono/Flux, backpressure, non-blocking I/O, reactive testing with StepVerifier).
• Proven experience building and operating high-volume REST APIs; working knowledge of SOAP/WSDL services and how to preserve or modernize them.
• Hands-on use of Cursor or an equivalent AI coding tool (GitHub Copilot, Claude Code, Windsurf, Amazon Q Developer) in production delivery, including configuring rules, context and prompts for a team.
• AWS or equivalent cloud (Azure, Google Cloud Platform): containers (EKS/ECS or Kubernetes), API gateways, IAM, secrets management, managed databases and caching, and infrastructure as code (Terraform or CloudFormation).
• CI/CD and automated testing: unit, integration, contract (e.g. Pact, Spring Cloud Contract) and performance testing.
• Security fundamentals for identity and customer data: OAuth 2.0/OIDC, encryption, PII handling, OWASP API Top 10.
• Observability: structured logging, metrics and distributed tracing (e.g. OpenTelemetry, Datadog, CloudWatch, Splunk).
• Knowledge of event streaming with Apache Kafka (producers/consumers, topic design, schema management) and large-scale distributed databases such as Apache Cassandra (data modeling, consistency tuning, reactive drivers).
• Sets technical direction and makes architectural trade-offs under delivery pressure, documenting decisions (ADRs) so others can follow them.
• Breaks a large migration into sequenced, estimable increments and keeps delivery predictable.
• Mentors engineers of mixed experience levels in reactive Java and AI-assisted engineering.
• Leads rigorous code and design reviews, holding AI-generated code to the same standard as human-written code.
• Manages risk proactively: identifies dependencies, failure modes and rollback plans before they become incidents.
• Works effectively with onshore/offshore and vendor or partner teams.
• Communicates clearly to both engineers and executives; can explain AI-assisted delivery outcomes in terms of speed, cost, quality and risk.
• Builds adoption of new ways of working, addressing skepticism with evidence.
• Manages stakeholders across product, architecture, security, QA and operations teams.
• Reports progress transparently, including what is not working, not just successes.
• Keeps skills current as AI tooling and practices evolve.
• Takes ownership of production incidents affecting customer-facing systems, from response through resolution and follow-up.

Preferred qualifications

• Experience with Play Framework and Akka (Scala or Java), especially reading and migrating legacy Play controllers, actors and routing.
• Has led or played a key role in an AI-assisted replatforming or modernization effort, with measurable results on speed or quality.
• Experience applying a structured AI development methodology such as AI-DLC or equivalent, or building team-level AI coding standards and governance.
• Background in customer identity, CIAM, profile or customer data platforms in regulated or high-availability environments.
• Familiarity with privacy and compliance requirements (e.g. SOC 2, PCI DSS, GDPR, CCPA).
• Experience with MCP servers, AI agents or custom tooling that connects AI coding assistants to internal systems and documentation.
• Relevant certifications such as AWS Certified Solutions Architect or Developer, or Spring Professional.
• Experience with behavior-driven development (BDD) and frameworks such as Cucumber, including setting the team's approach to executable specifications that verify behavioral parity during migration