Zuddl Hiring AI/ML Intern | India

About Zuddl

Zuddl is a cutting-edge modular platform for events and webinars, trusted by global organizations like Microsoft, Google, ServiceNow, Postman, and the United Nations. Our platform empowers B2B marketers and conference organizers with flexible, outcome-oriented event management solutions.

Backed by Y-Combinator (2020 batch), Zuddl has raised $13.35 million in Series A funding led by Alpha Wave Incubation and Qualcomm Ventures, alongside GrowX Ventures and Waveform Ventures.

Company: Zuddl
Location: Remote
Job Type: Internship (Full-time opportunities available based on performance)

What You’ll Do

• Prototype LLM-powered features using frameworks like LangChain and OpenAI Agents SDK for content automation and intelligent workflows.
• Build and optimize Retrieval‑Augmented Generation (RAG) systems, including document ingestion, chunking, embedding, and LLM integration.
• Work with vector databases to implement similarity search for use cases like intelligent Q&A and content recommendations.
• Experiment with prompt engineering and fine-tuning techniques.
• Deploy LLM-based microservices and agents using Docker, Kubernetes, and CI/CD best practices.
• Analyze model metrics, document findings, and propose data-driven improvements.
• Collaborate across teams, including product, design, and engineering, to align AI features with business goals and enhance user impact.

Requirements

• Strong proficiency in Python programming.
• Hands-on experience with large language models (LLMs)—building, fine-tuning, or applying them in projects.
• Familiarity with agentic AI frameworks like LangChain or OpenAI Agents SDK.
• Understanding of RAG architectures with practical experience.
• Experience with vector databases such as FAISS or OpenSearch.
• Portfolio of LLM-based projects (GitHub repositories, notebooks, or other coding samples).

Good to Have

• Ability to build full-stack web applications.
• Data analytics skills, including data manipulation (Pandas/SQL), visualization (Matplotlib/Seaborn/Tableau), and statistical analysis.
• Experience with tools like PostgreSQL, Metabase, or similar.
• Strong ML fundamentals: regression, classification, clustering, and deep learning techniques.
• Knowledge of recommender systems or hybrid ML solutions.
• Familiarity with deep learning frameworks like PyTorch or TensorFlow.
• Exposure to MLOps/DevOps tools such as Docker, Kubernetes, MLflow, or Kubeflow.