Engineer Software (Applied AI) [T500-28172]

What You'll Do

Write and review code; follow and learn the team's standards of excellence.

Evaluate user requests and help clarify objectives and functional requirements for new or modified programs.

Design and develop software and business-process solutions based on business needs, with guidance.

Contribute to AI-powered features — helping build Mutli-agent systems, LLMbacked functionality, RAG retrieval, and simple tool-using workflows under mentorship.

Help with basic quality checks for AI features (test prompts, sanity checks) so behavior stays predictable.

Conduct feasibility studies for new and existing applications, making decisions within defined parameters.

Focus on quality in all aspects of work and continually improve your standards.

Contribute to team activities and collaborate across teams to solve problems.

Core Engineering Experience (Required):

1–3 years as a Full Stack Developer across Java, Web, and Mobile technologies.

1+ years in OOAD, Java EE, Web technologies, and REST APIs.

Working knowledge of database technologies — relational (Oracle, Azure SQL,PostgreSQL) and/or NoSQL (MongoDB, Cassandra) — with exposure to mobile data synchronization and offline storage.

Working knowledge of UI/Web 2.0 development (JavaScript, CSS, SCSS, HTML5, AJAX, jQuery, NodeJS, ReactJS, AngularJS) with responsive, mobile-first design.

Foundational understanding of design patterns and building scalable, maintainable applications.

Familiarity with application servers (Tomcat, Jetty, Reactor Netty).

1+ years with cloud platform services (Azure, GCP, or AWS) and cloud application development.

Working knowledge of automated testing: JUnit/TestNG, plus Selenium, Playwright, Cucumber, Rest-Assured, Karate; mobile testing with Appium or Espresso.

Working knowledge of the full SDLC and Agile methodologies.

Exposure to CI/CD with Jenkins or GitHub Actions; Docker/Kubernetes is a plus.

Familiarity with GitHub Copilot or similar AI-assisted development tools.

Ability to understand stakeholder expectations and escalate issues that may affect delivery.

AI Skills (Mandatory):

AI-augmented engineering: Comfortable using AI tools (e.g., Copilot, ChatGPT/Claude code ) in daily engineering work to write, debug, and learn faster, with growing awareness of cost-aware usage.

LLM basics: Conceptual understanding of how LLM-based applications work (chatbots, Q&A, assistants), including the basics of prompt engineering and context.

RAG awareness: Familiarity with the idea of Retrieval-Augmented Generation — retrieving relevant data to ground LLM responses.

Agentic concepts: Awareness of agentic AI ideas — agents using tools, memory, and multi-step workflows.

AI frameworks (exposure): Hands-on exposure — through projects, coursework, or POCs — to an LLM framework such as LangChain, LangGraph, or Google GenAI tools.

Mandatory Skills Summary:

Languages: Java 11+

Databases: MySQL / PostgreSQL / Oracle / MongoDB / Cassandra

Frontend: HTML / CSS / JavaScript / ReactJS / Bootstrap

Backend: Spring Boot / REST APIs / Microservices

Automation Testing: JUnit / Selenium / Playwright / Cucumber / Jest

Cloud: AWS / GCP / Azure

DevOps: CI/CD, GitHub, Jenkins

AI: LLM basics, RAG awareness, agentic concepts, LangChain/LangGraph exposure