Lead Fullstack Ai Engineer Python Java

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)