LLM Engineer (Large Language Models)

Fospe UK Ltd; Bengaluru; Hybrid; INR 2,500,000 - 5,200,000; Full time

Benefits offered by this job

Competitive compensation with bonuses

Hybrid work at Bangalore Innovation Cn

Health, dental, wellness insurance

AI research learning stipend

MacBook Pro + equipment budget

Stock options

Job summary

Fospe is seeking an LLM Engineer to design, fine-tune, and operationalize cutting-edge Large Language Models and RAG pipelines for enterprise SaaS products.

You will collaborate with our AI research team to embed deterministic cognitive capabilities and high-throughput semantic reasoning, building scalable inference pipelines and guardrail systems.

Qualifications

• 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.

Responsibilities

• Architect, fine-tune, and quantize open-source foundation models 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.

Skills

LLM engineering

Python

asynchronous programming

Docker containerization

Linux/CUDA environments

Pytorch

Hugging Face Transformers

vLLM

LangChain/LlamaIndex

vector databases

Qdrant

Pinecone

Milvus

pgvector

Kubernetes

CUDA optimizations

Education

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related quantitative field

Tools

Docker

Kubernetes

Job description

About the Role

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.

Key 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 & Qualifications

• 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).

Primary Technologies

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

What We Offer

• Competitive compensation package with performance bonuses
• Flexible hybrid working schedule (Bangalore Innovation Center)
• Comprehensive health, dental, and wellness insurance coverage
• Generous annual compute and AI research learning stipend
• Latest Apple MacBook Pro M-series workstation & equipment budget
• Stock option and co-ownership opportunities