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-gappedAI 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.
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System Design: Architect the end-to-end lifecycle of AI products, from data ingestion and embedding generation to front-end delivery.
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Secure Deployment: Lead the transition of AI models from development to on-premise, air-gapped environments, ensuring zero external dependencies.
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Infrastructure Orchestration: Design and manage containerized microservices using Docker and Kubernetes (K8s) optimized for local hardware.
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Full-Stack Integration: Collaborate with engineering teams to integrate Python-based AI services with React-based frontends via high-performance APIs.
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Data Strategy: Implement and optimize Vector Databases and traditional relational databases to support RAG (Retrieval-Augmented Generation) workflows.
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Security First: Implement rigorous security protocols, including encryption at rest/transit, identity management, and model weight protection within restricted networks.
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Expertise in Python (FastAPI, Flask, or Django) for building scalable AI services.
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Deep understanding of Embeddings, Vector Search (e.g., Milvus, Qdrant, Weaviate), and LLM orchestration.
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Experience fine-tuning or deploying open-source models (Gemma, etc.) locally.
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Containerization: Mastery of Docker and orchestration via Kubernetes.
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Deployment: Proven track record of On-premise deployments and managing "Sneakernet" or air-gapped software update cycles.
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APIs: Experience designing secure, versioned RESTful or GraphQL APIs.
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React: Ability to architect how frontend applications consume complex AI streaming data.
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DB Management: Proficiency in PostgreSQL, NoSQL, and specialized Vector DBs.
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Experience with hardened Linux environments.
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Knowledge of network security in restricted environments (firewalls, proxy management, and certificate handling).
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Experience with GPU acceleration (CUDA/Triton) in local environments.
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Knowledge of MLOps tools adapted for offline use.
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Background in highly regulated industries (Defense, Healthcare, or Finance).
