About the Team

Machine Learning Platform team, builds the infrastructure and tools that enable scalable and efficient machine learning across the company. Our team is responsible for developing and maintaining the core ML infrastructure, including data pipelines, model training and serving frameworks, and feature stores. We also support large language model (LLM) deployment, enabling real-time retrieval, generation, and personalization. We work closely with product teams to deliver high-performance, reliable, and scalable machine learning solutions that drive business impact.

About the Role

As a Machine Learning Infrastructure Engineer, you will be responsible for designing, building, and optimizing data pipelines that feed into RAG systems. You will work with diverse data sources, building scalable indexing pipelines and ensuring high-performance data ingestion into vector databases. Your work will enable real-time retrieval and enhance the accuracy and efficiency of LLM-based applications.

You’re excited about this opportunity because you will… You thrive on zero-to-one projects and are excited about fast development cycles, driving quick iterations and impactful outcomes. Design and build high-performance, flexible data pipelines that can quickly adapt to new technologies, techniques, and modeling approaches for LLMs. Enhance the reliability, scalability, and observability of our data and inference infrastructure to support LLM-driven applications. Work closely with ML Engineers and Product Engineers to evolve the ML platform as per their use cases  Improve the reliability, scalability, and observability of our inference infrastructure. We’re excited about you because… You have industry experience: 5+ years of building ML infrastructure or related disciplines. You have a strong background in building and optimizing data pipelines in production. You understand the nuances of vector databases and their role in RAG systems. You are experienced in working with large-scale data infrastructure and real-time processing. You are a problem solver — you thrive on tackling complex technical challenges. You are passionate about working at the intersection of machine learning and data engineering. Nice To Haves Experience with popular vector databases such as Qdrant, Pinecone, or FAISS. Familiarity with modern LLM frameworks and how they interact with vector search. Experience with real-time data processing frameworks like Spark, Flink, or Kafka. Experience with cloud-based infrastructure (AWS, GCP, Azure) for data and model deployment. Knowledge of retrieval optimization techniques for large-scale systems.

 

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