AI Architect

Senior AI Systems Architect (On-Premise & Secure Environments) Role Overview We are looking for a visionary AI Architect to lead the design and deployment of a production-grade AI platform. Unlike standard cloud-based AI roles, you will be responsible for building high-performance, secure, and completely air-gapped AI ecosystems. You will bridge the gap between complex machine learning models and robust enterprise infrastructure, ensuring our product delivers cutting-edge intelligence without compromising data sovereignty. Key Responsibilities System Design: Architect the end-to-end lifecycle of AI products, from data ingestion and embedding generation to front-end delivery. Secure Deployment: Lead the transition of AI models from development to on-premise, air-gapped environments , ensuring zero external dependencies. Infrastructure Orchestration: Design and manage containerized microservices using Docker and Kubernetes (K8s) optimized for local hardware. Full-Stack Integration: Collaborate with engineering teams to integrate Python-based AI services with React-based frontends via high-performance APIs . Data Strategy: Implement and optimize Vector Databases and traditional relational databases to support RAG (Retrieval-Augmented Generation) workflows. Security First: Implement rigorous security protocols, including encryption at rest/transit, identity management, and model weight protection within restricted networks.

Technical Requirements AI & Data Science Expertise in Python (FastAPI, Flask, or Django) for building scalable AI services. Deep understanding of Embeddings , Vector Search (e.g., Milvus, Qdrant, Weaviate), and LLM orchestration. Experience fine-tuning or deploying open-source models (Gemma, etc.) locally. Architecture & DevOps Containerization: Mastery of Docker and orchestration via Kubernetes . Deployment: Proven track record of On-premise deployments and managing "Sneakernet" or air-gapped software update cycles. APIs: Experience designing secure, versioned RESTful or GraphQL APIs. Frontend & Databases React: Ability to architect how frontend applications consume complex AI streaming data. DB Management: Proficiency in PostgreSQL, NoSQL, and specialized Vector DBs. Security & Networking Experience with hardened Linux environments . Knowledge of network security in restricted environments (firewalls, proxy management, and certificate handling).

Preferred Qualifications

• Experience with GPU acceleration (CUDA/Triton) in local environments. Knowledge of MLOps tools adapted for offline use. Background in highly regulated industries (Defense, Healthcare, or Finance).