Senior HR Officer
We are looking for a highly skilled On-Premises AI Engineer to design, develop, and maintain AI-driven solutions. This role requires hands-on expertise in deploying and optimizing LLMs on-premises, managing the necessary AI infrastructure, and ensuring the scalability and reliability of AI systems.
Key Responsibilities
- Design, set up, and maintain the AI infrastructure needed for on-premises LLM deployments, ensuring efficiency and scalability.
- Deploy and optimize local LLMs (e.g., LLaMA 3, Mistral, Qwen) for different purposes such as RAG, Summarization, Text-To-Speech and Speech-To-Text and others.
- Implement and manage local vector databases and retrieval-augmented generation (RAG) pipelines.
- Ensure system security, data integrity, and high availability of AI services running on on-premises infrastructure.
- Experience with multi-GPU setups, ensuring efficient resource utilization for training and inference.
- Collaborate with IT and DevOps teams to automate deployment, monitoring, and maintenance of AI models and services.
- Troubleshoot and optimize containerized AI workloads using Docker and Kubernetes where necessary.
- Stay up-to-date with the latest LLM fine-tuning techniques, AI infrastructure advancements, and industry best practices.
Required Experience & Skills
- AI & LLM Expertise: At least 2 year of hands-on experience working with local LLMs, including local deployment (LLaMA 3, Mistral, etc.).
- Programming & Scripting: Proficiency in Python, C#, and C++ for AI model development and system integration.
- Infrastructure & Deployment: Experience in setting up and maintaining AI infrastructure on-premises, including hardware configurations, GPU utilization, and storage management.
- Data Handling: Strong experience with SQL databases (e.g., MS SQL) and vector databases for AI-powered retrieval.
- Security & Reliability: Experience in securing AI systems, managing access controls, and ensuring high availability.
- Education: A degree in Computer Science, AI, Machine Learning, or a related field.
Nice to Have
- Advanced LLM Fine-Tuning: Experience with LoRa, QLoRa, and other fine-tuning techniques for customizing models.
- System Administration: Expertise in Linux, Shell scripting, and containerized environments (Docker, Kubernetes optional).
- Version Control & CI/CD: Familiarity with Git and best practices for AI deployment pipelines.
- Cloud & Hybrid AI: Understanding of Azure AI Services for hybrid on-prem/cloud implementations.
- LLM Models Training: Hands-on experience in training LLMs on multi-GPU setups to optimize performance.
- Infrastructure Monitoring & Optimization: Experience with AI workload monitoring and optimization tools for resource efficiency.
