Zuddl is a modular platform for events and webinars that helps event marketers plan and execute events that drive growth. Event teams from global organizations like Microsoft, Google, ServiceNow, Zylo, Postman, TransPerfect and the United Nations trust Zuddl. Our modular approach to event management lets B2B marketers and conferences organizers decide which components they need to build the perfect event and scale their event program. Zuddl is an outcome-oriented platform with a focus on flexibility, and is more partner, less vendor..
FUNDING
Zuddl being a part Y-Combinator 2020 batch has raised $13.35 million in Series A funding led by Alpha Wave Incubation and Qualcomm Ventures with participation from our existing investors GrowX ventures and Waveform Ventures.
What you'll Do
- Prototype LLM-powered features using frameworks like LangChain, OpenAI Agents SDK to power content automation and intelligent workflows.
- Build and optimize Retrieval‑Augmented Generation (RAG) systems: document ingestion, chunking, embedding with vector DBs, and LLM integration.
- Work with vector databases to implement similarity search for use cases like intelligent Q&A, content recommendation, and context-aware responses.
- Experiment with prompt engineering and fine-tuning techniques
- Deploy LLM-based microservices and agents using Docker, K8s and CI/CD best practices.
- Analyze model metrics, document findings, and suggest improvements based on quantitative evaluations.
- Collaborate across functions—including product, design, and engineering—to align AI features with business needs and enhance user impact.
Requirement
- Strong Python programming skills.
- Hands-on with LLMs—experience building, fine-tuning, or applying large language models.
- Familiarity with agentic AI frameworks, such as LangChain or OpenAI Agents SDK (or any relevant tool).
- Understanding of RAG architectures and prior implementation in projects or prototypes.
- Experience with vector databases like FAISS, Opensearch etc.
- Portfolio of LLM-based projects, demonstrated via GitHub, notebooks, or other coding samples.
Good to Have
- Capability to build full‑stack web applications.
- Data analytics skills—data manipulation (Pandas/SQL), visualization (Matplotlib/Seaborn/Tableau), and statistical analysis. Worked with PostgreSQL, Metabase or relevant tools/databases.
- Strong ML fundamentals: regression, classification, clustering, deep learning techniques.
- Experience building recommender systems or hybrid ML solutions.
- Experience with deep learning frameworks: PyTorch, TensorFlow (or any relevant tool).
- Exposure to MLOps/DevOps tooling: Docker, Kubernetes, MLflow, Kubeflow (or any relevant tool).
Why You Want To Work Here
- Opportunity to convert to a Full-Time Role, based on performance and organisational requirements after the end of the internship tenure.
- A culture built on trust, transparency, and integrity
- Ground floor opportunity at a fast-growing series A startup
- Competitive Stipend
- Work on AI-first features in an event-tech startup with global customers
- Thrive in a remote-first, empowering culture fueled by ownership and trust
