AI Architect

THIS ROLE WILL BE ONSITE 5 DAYS PER WEEK.

We are seeking a Senior AI Engineer to direct the technical architecture and implementation of advanced Machine Learning and Large Language Model (LLM) systems across our portfolio of companies. The core appeal of this position is technical autonomy. You will have the authority to evaluate, select, and deploy cutting‑edge technologies and frameworks to build functional, high-performance AI solutions from the ground up.

• Cross-Portfolio Data and Automation:
  Architect systems to process, vectorize, and analyze heterogeneous data generated by our various businesses. The objective is to deploy models that extract operational insights and automate complex business logic.
• Platform Integration:
  Help build and integrate advanced AI capabilities into our proprietary Asset Management platform. This requires designing robust systems that allow end-users to query their operational data, generate insights, and configure highly customized automated actions within the platform.

• Architectural Design:
  Design end-to-end ML pipelines and LLM architectures tailored to specific, complex business use cases, exercising full discretion over the technology stack.
• Model Deployment & Optimization:
  Evaluate and implement open model weights. Optimize inference performance using engines such as llama.cpp to ensure low latency and cost-effective execution.
• Fine‑Tuning:
  Execute domain‑specific LLM fine‑tuning to handle proprietary, industry‑niche data securely and efficiently.
• RAG & Vectorization:
  Develop scalable Retrieval‑Augmented Generation (RAG) systems. Manage the full data pipeline including embedding model selection, complex data vectorization, and vector database administration.
• System Integration:
  Utilize the Model Context Protocol (MCP) to build secure, standardized connections between our AI models, internal data stores, and operational tools.
• Infrastructure Management:
  Define, provision, and manage the necessary hardware and compute infrastructure, including GPU clusters, NPUs, and relevant cloud AI services, for both training and inference workloads.

• Core Stack:
  Deep, hands‑on proficiency with core technologies such as Python, PyTorch, Tensor Flow, etc.
• LLM Engineering:
  Extensive practical experience working with open model weights and inference optimization tools like llama.cpp.
• Model Training:
  Demonstrated ability in LLM fine‑tuning methodologies (e.g., LoRA, QLoRA) for specialized tasks.
• Information Retrieval:
  Strong expertise in building and scaling RAG architectures, with a deep understanding of embeddings and data vectorization.
• Integration Protocols:
  Familiarity with the MCP (Model Context Protocol) standard for tool and data integration.
• Compute Infrastructure:
  Thorough understanding of the hardware requirements for ML workloads, including experience configuring and optimizing GPU clusters, NPUs, and related cloud services.

• Master’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or related field.
• 2+ years of experience in AI, machine learning, data science, or automation in a production environment.