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Fresher
Not Disclosed
  • Posted 18 hours ago
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Job Description

In this 20-24 weeks program, you will work as an AI Engineer Intern and gain hands-on experience building real-world AI solutions. You'll work closely with our Engineering and Research teams on impactful projects using technologies like documents automation, agentic frameworks, and machine learning.

You'll tackle practical challenges or explore cutting-edge ideas, with guidance from experienced mentors. By the end of your internship, you'll have contributed to a meaningful, production-ready project and gained valuable exposure to applied AI in a collaborative environment.

Roles & Responsibilities

  • Assist in design, prototype, and evaluate AI-based solutions using modern frameworks and techniques
  • Contribute to the development of systems involving agentic workflows, RAG pipelines, LLMs, and machine learning models
  • Explore innovative approaches that can lead to production deployment and/or publication-ready outcomes
  • Present findings, implementations, and recommendations clearly to both technical and non-technical stakeholders

Requirements

  • Currently pursuing a Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Electrical and Computer Engineering, Data Science, Business Analytics, Information Systems, or a related field
  • Strong programming skills in Python, solid understanding of AI, NLP, and large language models (LLMs)
  • Strong computer science fundamentals, with an understanding of model deployment, serving, and efficiency optimisation
  • Familiarity with domain-specific LLMs and techniques such as supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and model distillation is a plus
  • Hands-on experience with agentic frameworks, including MCP, tool use, and skills is a plus
  • Prior publication experience in top-tier conferences is a plus

More Info

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Key Skills

efficiency optimisation

model distillation

supervised fine-tuning (SFT)

large language models (LLMs)

model deployment

reinforcement learning from human feedback (RLHF)

agentic frameworks

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