Lead requirements analysis, solution design, development, testing, implementation, and production support of enterprise applications using Java, Spring Boot, and AWS
Design and develop scalable microservices, secure RESTful APIs, backend components, and integrations with internal and third-party systems
Build, deploy, monitor, and support cloud-based applications on AWS with appropriate resiliency, logging, performance, security, and cost controls
Provide technical leadership through architecture discussions, design reviews, code reviews, engineering standards, troubleshooting, root-cause analysis, documentation, and developer mentoring
Collaborate with product owners, architects, QA, DevOps, and onsite/offshore teams; contribute to Agile ceremonies, estimation, risk management, release readiness, and continuous improvement
Design, develop, and maintain automated test frameworks and test suites for distributed software systems, APIs, data pipelines, and enterprise applications.
Test microservices-based architectures, event-driven systems, and distributed applications.
Validate data movement, transformations, reconciliations, and end-to-end workflows across multiple systems.
Develop automated functional, integration, API, regression, and performance test solutions.
Integrate automated testing into CI/CD pipelines and Agile workflows.
Design how AI models plug into core systems and applications, integrating LLMs and AI frameworks to build RAG pipelines and connect AI into the broader tech stack.
Responsibilities
Design how AI models plug into core systems and applications
Integrate LLMs and AI frameworks using tools like LangChain or Semantic Kernel
Build RAG pipelines and work with vector databases
Set up APIs and microservices to connect AI into the broader tech stack
Evaluate AI platforms for cost, performance, and compliance fit
Design how AI models plug into our core systems and applications Integrate LLMs and AI frameworks using tools like LangChain or Semantic Kernel Build RAG pipelines and work with vector databases where needed Set up APIs and microservices to connect AI into the broader tech stack Evaluate AI platforms for cost, performance, and compliance fit Handle data privacy, model risk, and monitoring for anything in production