Job description

About the Role 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. What You'll Do: Provide technical leadership and mentorship to engineering teams while collaborating with architects, product managers, and UX designers to create innovative AI solutions that address complex customer challenges. Lead the design, development, and deployment of AI-driven features. Drive end-to-end ownership—from feasibility analysis and design specifications to execution and release—while ensuring quick iterations based on customer feedback in a fast-paced Agile environment. Spearhead technical design meetings and produce detailed design documents that outline scalable, secure, and robust AI architectures. Ensure that the solutions are aligned with long-term product strategy and technical roadmaps. 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. We'd Love for You to Have: 2 to 5 years of total experience and strong understanding of LLMs and their underlying principles — transformer architecture, attention mechanisms, and hyperparameter tuning. Proven experience designing and building AI agents, including multi-agent orchestration, tool-use patterns, multi-step planning, and agent memory architectures (short-term and long-term). Hands-on experience with agentic frameworks such as LangGraph, CrewAI, or AutoGen, and familiarity with RAG pipelines that integrate external knowledge sources (documents, databases, APIs). In-depth knowledge of vector databases and indexing algorithms; practical experience with FAISS, Pinecone, Weaviate, or Milvus. Experience with agent observability, tracing, and guardrails — tools like Langfuse or equivalent — to ensure reliability, safety, and debuggability of agentic systems. Proficiency in prompt engineering — crafting, iterating, and optimizing complex prompts for context-sensitive, domain-specific LLM outputs. Familiarity with Evals and other performance evaluation tools for measuring model quality, relevance, and efficiency. Proficiency in Python and experience with machine learning libraries such as TensorFlow, PyTorch, and Hugging Face Transformers. Experience with data preprocessing, vectorization, and handling large-scale datasets. Ability to present complex technical ideas and results to both technical and non-technical stakeholders. Nice-to-Have: 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, including data 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). Familiarity with programmatic advertising, RTB, or ad auction mechanics. Knowledge of MCP (Model Context Protocol) or similar tool-integration standards Publication or contributions to research in AI, LLMs, or related fields. Qualification: Should have a bachelor’s degree in engineering or an equivalent degree from a well-known institute/university. Additional Information: Return to Office : PubMatic employees throughout the globe have returned to our offices via a hybrid work schedule (3 days “in office” and 2 days “working remotely”) that is intended to maximize collaboration, innovation, and productivity among teams and across functions. Benefits: Our benefits package includes the best of what leading organizations provide, such as paternity/maternity leave, healthcare insurance, broadband reimbursement. As well, when we’re back in the office, we all benefit from a kitchen loaded with healthy snacks and drinks and catered lunches and much more!. Diversity and Inclusion : PubMatic is proud to be an equal opportunity employer; we don’t just value diversity, we promote and celebrate it. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status About PubMatic 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.

Requirements

Must-haves

Provide technical leadership and mentorship to engineering teams while collaborating with architects, product managers, and UX designers to create innovative AI solutions that address complex customer challenges.
Spearhead technical design meetings and produce detailed design documents that outline scalable, secure, and robust AI architectures.
Ensure that the solutions are aligned with long-term product strategy and technical roadmaps.
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.
2 to 5 years of total experience and strong understanding of LLMs and their underlying principles — transformer architecture, attention mechanisms, and hyperparameter tuning.
Proven experience designing and building AI agents, including multi-agent orchestration, tool-use patterns, multi-step planning, and agent memory architectures (short-term and long-term).
Hands-on experience with agentic frameworks such as LangGraph, CrewAI, or AutoGen, and familiarity with RAG pipelines that integrate external knowledge sources (documents, databases, APIs).
In-depth knowledge of vector databases and indexing algorithms; practical experience with FAISS, Pinecone, Weaviate, or Milvus.
Experience with agent observability, tracing, and guardrails — tools like Langfuse or equivalent — to ensure reliability, safety, and debuggability of agentic systems.
Proficiency in prompt engineering — crafting, iterating, and optimizing complex prompts for context-sensitive, domain-specific LLM outputs.
Familiarity with Evals and other performance evaluation tools for measuring model quality, relevance, and efficiency.

Nice-to-haves

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, including data 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).
Familiarity with programmatic advertising, RTB, or ad auction mechanics.
Knowledge of MCP (Model Context Protocol) or similar tool-integration standards
Publication or contributions to research in AI, LLMs, or related fields.

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Keywords to mirror

aiagilellmpythonmachine learningawsgcpazuredockerkubernetes

Platform: greenhouse
Effort: quick

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