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Software Engineer (AI-Powered Advertising Agents - AgenticOS)

PubMatic

Pune

2-10 years

Today

$21.7K–33.7K/yr

Full time

Hybrid

Skills Required LLM

RAG

Gen AI

Agentic AI

Vector Database

Prompt Engineering

Hugging Face Transformers knowledge graph embeddings

LangGraph

CrewAI

AutoGen

FAISS

Pinecone

Weaviate

Milvus

Description PubMatic is hiring a Principal/Senior/Software Engineer for AI-Powered Advertising Agents (AgenticOS) within Engineering. The role focuses on building and optimizing generative AI agents, RAG systems, vector search, and LLM-based solutions for customer-facing and internal products.

Company: PubMatic

Role: Principal/Senior/Software Engineer (AI-Powered Advertising Agents - AgenticOS)

Location: Pune, IN | Hybrid (3 days in office, 2 days remote)

Experience

- 2 to 10 years of total experience
- Strong understanding of LLMs, transformer architecture, attention mechanisms, and hyperparameter tuning
- Proven experience designing and building AI agents
- Experience with multi-agent orchestration, tool-use patterns, multi-step planning, and agent memory architectures
- Hands-on experience with agentic frameworks such as LangGraph, CrewAI, or AutoGen
- Familiarity with RAG pipelines that integrate external knowledge sources
- In-depth knowledge of vector databases and indexing algorithms
- Practical experience with FAISS, Pinecone, Weaviate, or Milvus
- Experience with agent observability, tracing, and guardrails
- Proficiency in prompt engineering for complex, context-sensitive LLM outputs
- Familiarity with Evals and other performance evaluation tools
- Proficiency in Python
- 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 technical and non-technical stakeholders

Qualification

- Bachelor’s degree in engineering or an equivalent degree from a well-known institute/university

Responsibilities

- Lead the design, development, and deployment of AI-driven features
- Own work end to end from feasibility analysis and design specifications through execution and release




- Iterate quickly based on customer feedback in an Agile environment
- Spearhead technical design meetings
- Produce detailed design documents for scalable, secure, and robust AI architectures
- Align solutions with long-term product strategy and technical roadmaps
- Implement and optimize LLMs for specific use cases
- Fine-tune models, deploy pre-trained models, and evaluate performance
- Develop AI agents powered by RAG systems
- Integrate external knowledge sources to improve accuracy and relevance of generated content
- Design, implement, and optimize vector databases for efficient and scalable vector search
- Work on vector indexing algorithms
- Create and refine prompts to improve LLM output quality
- Use evaluation frameworks and metrics to assess and improve generative models and AI systems
- Collaborate with data scientists, engineers, and product teams
- Integrate AI-driven capabilities into customer-facing products and internal tools
- Stay current with research and trends in LLMs, RAG, and generative AI
- Monitor and optimize models for performance, scalability, and cost efficiency

Additional Responsibilities

- Work in a fast-paced Agile environment
- Ensure solutions are scalable, secure, and robust
- Support reliability, safety, and debuggability of agentic systems through observability and guardrails
- Build AI agents using graph-based architectures
- Work with knowledge graph embeddings and graph neural networks
- Train small base models using custom data
- Handle data collection and pre-processing for domain-specific fine-tuning
- Deploy AI models on cloud platforms
- Use containerization technologies for deployment
- Work in the domain of programmatic advertising and ad auction mechanics
- Contribute to or publish research in AI, LLMs, or related fields
- Hybrid work schedule with 3 days in office and 2 days remote
- Benefits include paternity/maternity leave,



healthcare insurance, broadband reimbursement, snacks, drinks, and catered lunches
- Equal opportunity employer with diversity and inclusion commitment

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

More Skills Retrieval-Augmented Generation (RAG), large language models (LLMs), transformer architecture, attention mechanisms, hyperparameter tuning, multi-agent orchestration, tool-use patterns, multi-step planning, agent memory architectures, Langfuse, Evals, Python, TensorFlow, PyTorch, data preprocessing, vectorization, large-scale datasets, graph neural networks (GNNs), AWS, GCP, Azure, Docker, Kubernetes, programmatic advertising, RTB, ad auction mechanics, MCP (Model Context Protocol)

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