AI Engineer (RAG & On Prem Llms)

DCG is a company that connects business needs with people's ambitions, helping candidates find suitable environments for growth and assisting companies in building effective teams. They focus on close collaboration and listening to create lasting and valuable relationships.

• 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
• Bachelor's, Master's, or PhD degree in Computer Science, Artificial Intelligence, or a related field
• Knowledge of English (B2+/C1)

• 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