Job Description
About The Opportunity
Industry: Enterprise Generative AI & Natural Language Processing (NLP). We build LLM-driven search, knowledge augmentation, and intelligent automation solutions for B2B SaaS and enterprise customers. The team focuses on production-grade Retrieval-Augmented Generation (RAG), embedding pipelines, and low-latency inference services that power customer-facing products and internal automation.
Standardized Title: Machine Learning Engineer — LLM & RAG (best-performing title for this search)
Role & Responsibilities
Industry: Enterprise Generative AI & Natural Language Processing (NLP). We build LLM-driven search, knowledge augmentation, and intelligent automation solutions for B2B SaaS and enterprise customers. The team focuses on production-grade Retrieval-Augmented Generation (RAG), embedding pipelines, and low-latency inference services that power customer-facing products and internal automation.
Standardized Title: Machine Learning Engineer — LLM & RAG (best-performing title for this search)
Role & Responsibilities
- Design and implement end-to-end RAG pipelines: document ingestion, embedding generation, vector indexing, retrieval, and prompt orchestration for production LLM applications.
- Fine-tune, evaluate, and optimize LLMs and embedding models to meet task-specific accuracy, latency, and cost targets.
- Build scalable Python services to expose inference and retrieval through secure REST APIs and...
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