T Hub - AI Expert – RAG & LLM Systems

architect, implement, and optimize end‑to‑end Retrieval Augmented Generation (RAG) pipelines for enterprise use cases in on‑premises environments
design and integrate retrieval mechanisms (e.g. vector databases such as Neo4j) with generative models (e.g. LLAMA 3.2, Mistral)
fine‑tune and optimize retrieval and generation components to achieve high accuracy and low latency
implement and customize inference servers using vLLM and LiteLLM for efficient and scalable LLM serving
integrate open‑source large language models with proprietary data sources and enterprise APIs
design GPU‑optimized, scalable on‑prem infrastructure for model training and inference, ensuring security and data governance compliance
collaborate with DevOps teams to containerize workflows using Docker and Kubernetes and automate MLOps pipelines
apply performance optimization techniques such as quantization, pruning, and dynamic batching
monitor system performance, troubleshoot bottlenecks, and ensure high availability
work closely with data engineers and business stakeholders to translate business requirements into technical AI solutions in telco environments

Bachelor’s, Master’s, or PhD degree in Computer Science, Artificial Intelligence, or a related field
at least 3 years of professional experience in ML/NLP roles, including 2+ years working with RAG systems
proven experience deploying and operating LLM‑based solutions in on‑prem or hybrid environments
hands‑on experience with vLLM, LiteLLM, and open‑source LLMs such as LLAMA 3.2, DeepSeek, or Mistral
strong Python skills and experience with frameworks such as PyTorch, Hugging Face Transformers, and LangChain
experience with vector databases (e.g. Neo4j)
familiarity with Linux‑based systems and Red Hat OpenShift
strong problem‑solving and analytical skills
ability to clearly communicate complex AI concepts to non‑technical stakeholders

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