Overview
We are seeking a Lead Software Engineer (LSE) to serve as a key technical contributor responsible for designing, developing, testing, and deploying enterprise-grade software solutions.
This role spans full-stack development and modern Generative AI solution design, requiring strong collaboration with business stakeholders and technology teams to deliver scalable, secure, and innovative solutions.
The ideal candidate combines deep engineering expertise with a self-starter mindset and the ability to execute quickly in agile environments.
Key Responsibilities
Lead complex technical initiatives across full-stack and AI-enabled solutions
Design and develop scalable microservices using Java, Spring, and Spring Boot
Build modern, responsive single-page applications using React, TypeScript, and related frameworks
Develop and integrate secure APIs (REST and GraphQL)
Design and implement Generative AI solutions using modern frameworks and tools
Collaborate closely with product, architecture, and DevOps teams for end-to-end delivery
Apply observability practices including logging, monitoring, and tracing
Mentor and guide engineers while promoting best practices and engineering excellence
Drive architectural decisions aligned with enterprise standards
Required Qualifications
Strong experience developing single page applications using Python, Java, JavaScript, and TypeScript
Strong experience with modern frameworks such as React.js, Next.js, Tailwind, and Bootstrap
Strong experience designing and developing enterprise-grade applications
Hands-on experience with Java and Spring/Spring Boot for backend development
Strong understanding of API design and integration (REST, GraphQL)
Experience with version control systems such as Git
Strong problem-solving skills and ability to lead technical design decisions
Generative AI Expertise
Strong hands-on experience building Generative AI applications
Experience with ADK, OpenAI Agent frameworks, LangChain, LangGraph, or similar tools
Understanding of LLM concepts, prompt engineering, and Retrieval-Augmented Generation (RAG)
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