Agentic AI Engineer Intern

About the Role

This internship operates at the intersection of Agentic AI, LLM applications, AI evaluation, conversational intelligence, and product intelligence.

Key Responsibilities

• Use Veyrai as a real candidate, attend interviews, and critically analyze the product from end to end.
• Responsibilities expand based on demonstrated skill level after selection:
• Test AI interview flows and evaluate multi-turn conversational responses.
• Identify subtle failure modes, hallucinations, and stale context triggers.
• Design rigorous AI evaluation scenarios and benchmark datasets.
• Investigate autonomous agent tool calling and code-execution sandboxes.
• Refine context pipelines, prompt structures, and interview rubrics.
• Build lightweight internal AI evaluation scripts and utilities.
• Analyze real candidate transcripts to improve follow-up adaptivity.
• Research modern agentic patterns (reflection, planning, and memory).

Requirements & Tech Stack

Core Mindset

• Problem identification and defect logging
• Analytical thinking and deductive reasoning
• Clear, structured technical communication
• Product empathy and candidate user experience
• Debugging mindset: dissecting why code or models fail

AI Fundamentals (Basic conceptual familiarity with)

• LLMs
• Prompting
• Embeddings
• RAG
• AI Agents
• Tool Calling
• Context Windows
• Memory
• AI Evaluation

Technical Basics (Basic knowledge of one or more)

• Python
• JavaScript / TypeScript
• REST APIs
• JSON
• Git / GitHub
• Databases
• Web Architecture

AI Tool Fluency

• Comfort experimenting with ChatGPT, Claude, Gemini, Cursor, or Copilot.
• Understanding of when to use an AI tool, how to verify its output, and how to use it responsibly.

Qualifications

• No previous jobs required.
• No previous internships, prior AI company experience, open-source pedigree, or published research papers needed.
• Observation: Ability to understand a real product and discover where it breaks.
• Root Cause: Ability to explain why problems happen and who they affect.
• Reasoning: Ability to think through pragmatic, realistic engineering solutions.
• Agility: Ability to learn new AI concepts rapidly and apply them with precision.

Candidate Roadmap (What You Must Do Before Applying)

• Step 01: Create Your Profile
• Sign up for Veyrai and complete your candidate profile, target roles, and resume details.
• Step 02: Explore the System
• Navigate through the dashboard, coding arena, system design canvas, and settings.
• Step 03: Complete ≥ 2 Interviews
• Experience at least two full interviews (e.g., Technical, Live Coding, System Design, or Behavioral).
• Step 04: Investigate Deficits
• Actively look for UX friction, latency, repetitive questions, missing features, and failure modes.
• Step 05: Analyze Root Causes
• Document what happened, why it matters, who is affected, and what Veyrai should do instead.
• Step 06: Submit Findings
• Fill out the comprehensive application form and submit your structured product investigation.

Assessment Standards

• Product Observation: Ability to discover meaningful, high-impact issues (edge cases, subtle UX friction, state desyncs, platform gaps).
• Problem Analysis: Ability to explain the root problem, not just superficial symptoms (root cause analysis, systemic understanding, user impact).
• Technical Understanding: Basic understanding of software architecture, APIs, and client-server flow (REST/WebSockets, data models, latency & performance, debugging mindset).
• AI Understanding: Understanding of fundamental concepts around LLMs, agents, context, tools, and evaluation (context windows, RAG & embeddings, tool execution, hallucination mitigation).
• Problem Solving: Ability to propose realistic, technically feasible solutions (pragmatic engineering, agentic patterns, UX remediation).
• Communication: Ability to clearly communicate technical findings in structured, precise language (clarity of thought, precise terminology, constructive tone).
• Aptitude & Logical Reasoning: Basic reasoning, deductive ability, and systematic inquiry (deductive logic, pattern recognition, prioritization rationale).

Compensation Model

Stipend

• Performance-based stipend — determined after the evaluation and interview process based on demonstrated technical understanding, problem-solving ability, AI knowledge, communication, and interview performance.