LLM & Language ModelEngineering Jobs
Browse active LLM engineer jobs in RAG, inference, fine-tuning, evaluation, and generative AI. Direct links to roles at AI companies, updated daily.
VMware Infrastructure SME with experience in VMware-to-Cloud transformation programs(Clients IBM, DBS)
Customer Success Account Manager | Digital Transformation and Portfolio Management
Java Developer(Spring Boot, AWS Bedrock , Snowflake, AWS EMR, RAG, Trino, OpenSearch)
LLM engineering is the fastest-growing specialization in AI. Companies need engineers who can build, fine-tune, and deploy large language models for production applications. From RAG pipelines and agent frameworks to inference optimization and evaluation systems, LLM engineers are at the center of the generative AI revolution.
Top AI labs like OpenAI, Anthropic, and Mistral are actively hiring, alongside hundreds of startups building LLM-powered products. Key skills include PyTorch, HuggingFace Transformers, LangChain, vLLM, and experience with fine-tuning techniques like LoRA and DPO. Strong Python fundamentals and understanding of transformer architectures are essential.
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Frequently Asked Questions
What does an LLM engineer do?
LLM engineers build and deploy large language model applications. This includes fine-tuning models for specific domains, building RAG (retrieval-augmented generation) pipelines, optimizing inference performance, implementing evaluation frameworks, and developing agent-based systems. They bridge the gap between ML research and production applications.
What skills are needed for LLM engineering jobs?
Core skills include Python, PyTorch, and deep understanding of transformer architectures. Experience with HuggingFace Transformers, LangChain or LlamaIndex, vector databases (Pinecone, Weaviate), and inference serving (vLLM, TensorRT-LLM) is highly valued. Understanding of fine-tuning methods (LoRA, QLoRA, DPO) and evaluation techniques is increasingly important.