LLM Engineer (Large Language Models)

Job Summary

Architect, fine-tune, and deploy large language models (LLMs) and advanced retrieval-augmented generation (RAG) search pipelines for enterprise platforms.

As an LLM Engineer at Fospe, you will be at the forefront of designing, fine-tuning, and operationalizing cutting-edge Large Language Models and Retrieval-Augmented Generation (RAG) pipelines. You will collaborate directly with our AI research team to embed deterministic cognitive capabilities and high-throughput semantic reasoning into our vertical enterprise SaaS products.

Responsibilities

• Architect, fine-tune, and quantize open-source foundation models (Llama 3, Mistral, Qwen, DeepSeek) for domain-specific enterprise workloads.
• Design high-accuracy Retrieval-Augmented Generation (RAG) architectures with hybrid dense/sparse search, reranking, and semantic chunking.
• Build low-latency streaming inference pipelines utilizing vLLM, TensorRT-LLM, and Triton Inference Server.
• Implement guardrail validation, safety filters, prompt routing, and automated hallucination benchmarking.
• Collaborate with backend engineers to expose scalable gRPC and REST interfaces for seamless frontend consumption.

Requirements

• Bachelor s or Master s degree in Computer Science, Artificial Intelligence, Data Science, or related quantitative field.
• 3+ years of professional experience in machine learning and deep learning, with 1+ years dedicated to LLM engineering.
• Deep hands-on expertise with PyTorch, Hugging Face Transformers, vLLM, and LangChain/LlamaIndex.
• Proven experience with vector databases such as Pinecone, Qdrant, Milvus, or pgvector.
• Strong proficiency in Python, asynchronous programming, Docker containerization, and Linux/CUDA environments.
• Demonstrated understanding of model optimization techniques including LoRA, QLoRA, AWQ, and GPTQ quantization.

Preferred Qualifications

• Experience with multi-agent orchestration frameworks (LangGraph, CrewAI, AutoGen).
• Contributions to open-source AI libraries or published research in NLP/LLMs.
• Experience deploying models on Kubernetes and cloud infrastructure (AWS/GCP/Azure GPU instances).

Tech Stack

• Python
• PyTorch
• Hugging Face
• vLLM
• LangChain
• LlamaIndex
• Qdrant
• Docker
• Kubernetes
• CUDA
• FastAPI

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