Early Talent - AI Native Engineer

Key Responsibilities

• Collaborate with engineers, architects, and product teams to design, build, test, and deploy custom applications and services
• Develop API-driven and platform-based solutions across front-end and back-end using your primary language (Python, Java, or TypeScript)
• Work in Agile delivery models (Scrum, Lean, XP) contributing reliably to sprint cycles and communicating progress clearly
• Use AI coding assistants daily as a standard part of delivery — actively, frequently, and with demonstrable impact on your productivity and output quality
• Apply AI across the software delivery lifecycle: AI-generated tests, AI-assisted debugging, AI-accelerated code review, and prompt engineering for development tasks
• Validate AI-generated outputs — check for errors, limitations, and hallucinations; develop sound judgment about when AI output is reliable and when human verification is required
• Contribute to proofs of concept, technical documentation, and solution development alongside senior engineers
• Engage in client-facing environments and learn to communicate technical concepts clearly to non-technical stakeholders
• Develop your understanding of how agentic systems work — how agents are orchestrated, how LLMs are called, and how software connects to AI pipelines — through on-the-job exposure and active self-development

Basic Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, or a related field
• Foundational programming experience in at least one primary language: Python, Java, TypeScript, or Node.js
• Basic understanding of web technologies including JavaScript, HTML, and CSS
• Academic projects, internships, or personal projects in software development that demonstrate you can build and ship working code
• Understanding of Agile delivery fundamentals
• Active and frequent use of AI tools in day-to-day study or work — successful candidates can demonstrate practical, everyday examples of how they leverage AI to solve problems, accelerate learning, and improve their output
• Sound judgment when using AI: ability to validate outputs, understand limitations, and show curiosity about how AI is reshaping modern software engineering
• Demonstrated curiosity about agentic AI — evidence of self-directed exploration through personal projects, side builds, online courses, or experimentation with tools like LangChain, LangGraph, AutoGen, or equivalent; we are looking for inclination and initiative, not production experience

Preferred Skills & Exposure

• Some exposure to LLM APIs in any context (personal projects, coursework, or work)
• API and service-oriented architecture
• Databases — SQL or NoSQL
• Cloud platforms (AWS, Azure, or GCP) and DevOps concepts
• Frontend frameworks such as React or Angular
• Containers (Docker) and CI/CD pipeline basics