Senior AI Deployment Engineer

About the Role

We are seeking a Senior AI Deployment Engineer to lead the architecture and delivery of AI-native applications in complex, high-security environments. This is a "Master Builder" role that bridges the gap between state-of-the-art LLM development and hardened, on-premises hardware infrastructure. You will be the primary technical authority for deploying mission-critical AI agents and RAG pipelines within sovereign data infrastructures and air-gapped environments.

Key Responsibilities

  • External Intelligence Strategy: Define and lead the strategy for sourcing, structuring, and operationalizing external intelligence data across OSINT, social platforms, dark web, and advertising domains for strategic, tactical and operational profiling and tracking of identities.
  • Data Source Identification: Identify high-value data sources and collection approaches aligned with investigative, intelligence, and analytical use cases.
  • Platform Alignment: Ensure alignment between intelligence data capabilities and broader investigative or analytical platforms.
  • On-Prem AI Architecture: Design and deploy end-to-end AI solutions—from prototype to production—specifically optimized for on-premise data centers and private clouds.
  • Hardware & Networking Optimization: Lead the selection and optimization of hardware (GPUs/TPUs, high-performance storage) and networking configurations (RDMA, InfiniBand, load balancing) to ensure low-latency model inference.
  • Hybrid RAG & Agentic Systems: Build advanced Retrieval-Augmented Generation (RAG) and multi-capable AI agents using models like GPT-4, Claude, and Gemini, while ensuring they function seamlessly within local security constraints.
  • Model Orchestration: Fine-tune and deploy open-source LLMs (Llama, Mistral, etc.) on local infrastructure, optimizing for memory efficiency and throughput.
  • Customer Engagement & Advisory: Act as the technical face for customers, educating their executive and engineering teams on best practices for AI deployment, data privacy, and infrastructure scaling.
  • Mentorship: Provide technical leadership and mentorship to junior engineers, fostering a culture of high-performance shipping and continuous learning.
  • System Resilience: Architect highly available, scalable AI-native infrastructure using Kubernetes and containerization in on-prem environments.

Required Experience & Capabilities

  • 5+ years of proven experience in AI Systems Engineering, Data Infrastructure, or High-Performance Computing (HPC).
  • Deep understanding of bare-metal deployments, hardware-level optimization (NVIDIA/AMD), and localized networking configurations.
  • Hands-on experience with LangChain, LangGraph, or LlamaIndex for building complex agentic workflows.
  • Expertise with vector databases and managing real-world "messy" datasets through robust ETL pipelines.
  • Proficiency in Python, Kubernetes, Docker, and modern CI/CD for on-premise or hybrid cloud environments.
  • Exceptional verbal and written communication skills with a proven ability to simplify complex technical concepts for customers.

Nice-to-Have

  • Experience in multi-agent systems and AI observability frameworks.
  • Background in highly regulated industries such as Defense, Energy, or Finance.
  • Familiarity with air-gapped security protocols and sovereign data infrastructure.

Key Skills

  • AI/ML: LLMs, RAG, Semantic Search, Fine-tuning, Multi-Agent Systems.
  • Infrastructure: Hardware Acceleration (GPUs), Networking Optimization, Kubernetes, On-Prem Deployment, Data Infrastructure.
  • Engineering: Python, ETL, Vector Databases, LangChain, LangGraph.

How to Apply

Submit your resume and a cover letter outlining your experience

Apply Now