Pubmatic Recruitment Drive 2025 | Pubmatic Hiring Software Engineer

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PubMatic is one of the world’s leading scaled digital advertising platforms, offering more transparent advertising solutions to publishers, media buyers, commerce companies and data owners, allowing them to harness the power and potential of the open internet to drive better business outcomes.
Founded in 2006 with the vision that data-driven decisioning would be the future of digital advertising, we enable content creators to run a more profitable advertising business, which in turn allows them to invest back into the multi-screen and multi-format content that consumers demand.

PubMatic is looking for engineers with expertise in Generative AI and AI agent development.
You will be responsible for building and optimizing advanced AI agents that leverage the latest technologies in Retrieval-Augmented Generation (RAG), vector databases, and large language models (LLMs).
You will work on developing state-of-the-art solutions that enhance Generative AI capabilities and enable our platform to handle complex information retrieval, contextual generation, and adaptive interactions.

Should have a bachelor’s degree in engineering (CS / IT) or equivalent degree from a well-known Institutes / Universities.
Experience in building AI agents using graph-based architectures, including knowledge graph embeddings and graph neural networks (GNNs).
Experience with training small base models using custom data, includingdata collection, pre-processing, and fine-tuning models to specific domains or tasks.
Familiarity with deploying AI models on cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
Publication or contributions to research in AI, LLMs, or related fields.

Collaborate with engineers, architects, product managers, and UX designers to develop innovative AI solutions for new customer use-cases.
Work independently and iterate quickly based on customer feedback to make product tweaks.
Implement and optimize LLMs for specific use cases, including fine-tuning models, deploying pre-trained models, and evaluating their performance.
Develop AI agents powered by RAG systems, integrating external knowledge sources to improve the accuracy and relevance of generated content.
Design, implement, and optimize vector databases (e.g., FAISS, Pinecone, Weaviate) for efficient and scalable vector search, and work on various vector indexing algorithms.
Create sophisticated prompts and fine-tune them to improve the performance of LLMs in generating precise and contextually relevant responses.
Utilize evaluation frameworks and metrics (e.g., Evals) to assess and improve the performance of generative models and AI systems.
Work with data scientists, engineers, and product teams to integrate AI-driven capabilities into customer-facing products and internal tools.
Stay up to date with the latest research and trends in LLMs, RAG, and generative AI technologies to drive innovation in the company’s offerings.
Continuously monitor and optimize models to improve their performance, scalability, and cost efficiency