[BD] AI Engineer Intern (AI-Native Global Product | Agentic AI | Real-World Enterprise Systems) - 6 months Internship

Recommendation Letter for 6-months internship program from your University is a MUST for this job.

🚀 About This Internship

This is not a research internship.
This is not a theoretical AI role.

This is hands-on AI engineering inside a global product at scale.

Our workflow platform—serving 500,000+ users worldwide—is evolving into a fully AI-native system. Built on advanced low-code/no-code (NCLC) architecture and accelerated by Generative AI, we are redefining how enterprise applications are designed, generated, validated, and scaled.

We are building agentic AI systems that can generate entire workflow applications:

• Business logic
• User interfaces
• System integrations

Your role is to ensure these systems actually work—accurately, reliably, and at scale.

If you want to become a top-tier AI engineer in 2–3 years, this is your acceleration path.

🎯 Your Mission

Assist to Build and validate AI-generated applications—and make them trustworthy for enterprise use.

You will work where AI meets reality:

• Real systems. Real users. Real constraints
• Accuracy and correctness over demos
• Engineering discipline applied to AI outputs

🧩 What You’ll Work On

You will operate at the intersection of AI Engineering × Software Engineering × Product Reality.

Core Responsibilities

• Work with product leaders and domain experts to understand real workflows, rules, and edge cases
• Build and improve Generative AI and agentic AI systems
• Deep dive into legacy source code and compare with AI-generated outputs
• Design and execute rigorous validation strategies across:
  • Business logic and rules
  • UI behavior
  • APIs and integrations
  • Data handling and security constraints
• Train and refine AI agents through structured feedback loops
• Generate, test, and optimize AI-produced code with engineering-level precision
• Contribute to prompt engineering, system tuning, and model improvement
• Support deployment, monitoring, and optimization in production
• Collaborate across engineering, QA, and business teams to ensure end-to-end quality

Extended Scope (Based on Strengths)

• Assist in applied ML / GenAI model improvements
• Contribute to MLOps pipelines (CI/CD, monitoring, evaluation)
• Improve data pipelines and integrations for model reliability

🧠 Who We’re Looking For

We are looking for builders—not observers.

Must-Have

• Pursuing a degree in Computer Science, Software Engineering, Data Science, or related
• Strong foundation in software engineering fundamentals
• Solid understanding of AI/ML, deep learning, and Generative AI
• Proficiency in Python and familiarity with LLM frameworks
• Strong problem-solving and structured thinking
• High attention to detail and patience in validating AI outputs
• Ability to deep dive into code and systems end-to-end
• Strong communication skills, including working with non-technical stakeholders

What Makes You Stand Out

• You understand that domain knowledge is as critical as models in AI systems
• You thrive in ambiguity and complexity
• You are willing to go deep into edge cases and failure scenarios
• You proactively extract knowledge from experts and convert it into testable logic
• You think like an engineer but operate with a product mindset

Preferred

• Experience with cloud platforms (AWS, Azure, or GCP)
• Familiarity with Docker, Kubernetes, Git, CI/CD
• Basic understanding of MLOps practices
• Exposure to data pipelines and enterprise systems
• Experience with AI-assisted coding tools / copilots

✅ What “Great” Looks Like 

Quality & Validation

• Increased scenario coverage, including edge cases
• Measurable accuracy improvements in AI-generated outputs
• Early detection of hidden defects
• Clear traceability from business rules → test cases → results
• Creation of reusable validation assets

Engineering & Delivery

• Faster validation cycles without quality compromise
• Strong use of automation where applicable
• High-quality, structured documentation
• Strong ownership, reliability, and follow-through

🌍 What You Will Gain

• Hands-on experience building AI systems used in real enterprise products
• Exposure to agentic AI architecture at scale
• Deep understanding of AI in complex business environments
• Experience with real software delivery (Agile, CI/CD, quality systems)
• Mentorship from senior engineers, product leaders, and domain experts

💡 Why This Role Is Different

Most AI roles focus on models.
This role focuses on making AI work in the real world.

• You won’t just generate code—you will prove it works in all scenarios
• You won’t just build AI—you will make it trustworthy
• You won’t just ship outputs—you will enforce correctness and reliability

This is where real AI engineers are built.

🧭 Culture & Expectations

We invest in interns who demonstrate:

• Ownership – drive outcomes, not tasks
• Professionalism – clear, structured communication
• Execution focus – results over activity
• Energy & passion – intensity to learn and deliver
• Team mindset – collaborate to win
• Curiosity – continuously improve

🔥 Internship → Full-Time Opportunity

Outstanding interns may convert to Full-Time AI Engineer roles (external contract).

We invest in those who:

• Take ownership
• Deliver measurable results
• Continuously improve
• Build trust through quality and reliability

📌 Application Requirements (Mandatory)

1. Motivation Letter (1 Page)

Include:

• Why this role (real-world AI engineering), why you, why now
• What you have built, tested, or validated (projects accepted)

2. Written Response (Core Question)

“How would you design a validation strategy to ensure maximum scenario coverage and achieve near-perfect accuracy for AI-generated applications in a complex enterprise system?”

Optional (Strongly Recommended)

• GitHub / portfolio / demos
• Proof of execution (projects, scripts, automation, notebooks)

🧠 Final Note

AI is changing how software is built.

But building AI is not enough.

The future belongs to engineers who can make AI systems accurate, reliable, and usable in complex real-world environments.

If you want to be one of them—this is where you start.

Equal Opportunity
We are committed to building diverse, inclusive teams and encourage all qualified candidates to apply.