Dark Wolf constructs and deploys data management and analytics solutions for the defense and intelligence communities. We’re proud to boast a world-class engineering team that thrives on rolling up their sleeves to solve your mission’s biggest challenges.

Dark Wolf is seeking an elite AI Software Architect (SME) to drive the vision, structural topology, and enterprise-wide architectural execution of mission-critical Artificial Intelligence and Machine Learning systems. Operating at the apex of software engineering and hardware acceleration, you will design fault-tolerant, resilient, and ultra-low-latency distributed AI architectures deployed across air-gapped, multi-cloud, and tactical edge environments.

In this role, you will serve as the principal technical authority, bridging high-assurance systems engineering with bleeding-edge AI models, custom inference engines, multi-agent frameworks, and zero-trust DevSecOps pipelines.

Key Responsibilities

Enterprise AI System Design: Architect end-to-end distributed AI platforms, high-throughput model inference pipelines, and scalable enterprise LLM/SLM deployment topologies tailored for classified enclaves.
Autonomous & Agentic Systems: Design resilient multi-agent orchestration engines, continuous Retrieval-Augmented Generation (RAG) platforms, and real-time semantic routing layers using modern framework paradigms.
Hardware & Inference Optimization: Lead system trade studies to optimize compute across heterogeneous hardware (GPUs, TPUs, NPUs), implementing advanced quantization, speculative decoding, and custom execution kernels for edge and air-gapped environments.
Zero-Trust Security & Governance: Establish system-wide AI security postures, incorporating automated DevSecOps, prompt-injection guardrails, differential privacy, and rigorous supply-chain risk management for ML artifacts.
Technical Authority & Roadmap Strategy: Interface directly with executive leadership and intelligence community stakeholders to map mission objectives to technical architectures, establish enterprise coding and safety standards, and direct R&D initiatives.

Required Qualifications

A Bachelor's degree in Computer Science, Information Systems, Engineering, or a related technical field (Master’s degree or Ph.D. strongly preferred).
15+ years of software engineering and systems architecture experience, with demonstrated leadership in delivering enterprise-scale AI/ML solutions.
AI Frameworks & LLMOps: Advanced mastery of low-level framework mechanics (PyTorch, TensorRT-LLM, vLLM, DeepSpeed, Ray), custom extension development, and enterprise orchestration platforms (LangGraph, AutoGen, LlamaIndex).
AI Models & Fine-Tuning Strategy: Expertise in novel architecture adaptation, speculative decoding, mixture-of-experts (MoE), parameter-efficient fine-tuning (LoRA, QLoRA), and post-training alignment (RLHF, DPO, GRPO).
Machine Learning Systems Engineering: MLOps/LLMOps architecture, model governance, continuous training pipelines, real-time drift detection, and deterministic evaluation frameworks.
Systems Programming & Performance: Polyglot mastery in Java, Rust, Python, and C, with deep expertise in asynchronous execution, memory management, CUDA/Triton kernels, and hardware-level performance profiling.
Containerization & Mesh Orchestration: Enterprise Kubernetes multi-cluster federation, custom CRDs, Service Mesh (Istio), bare-metal GPU scheduling, and zero-trust containerization strategies.
Multi-Cloud & Air-Gapped Infrastructure: Cross-cloud architecture (AWS GovCloud, Azure Secret), Infrastructure as Code (Terraform, Pulumi), and disconnected/air-gapped tactical node deployment methodologies.
DevSecOps & AI Security Tooling: Designing automated SAST/DAST pipelines, confidential computing enclaves (TEEs), runtime guardrails, adversarial AI defense, and automated vulnerability remediation frameworks.
Agile & Enterprise Transformation: Steering multi-pod engineering teams, establishing SAFe/Scaled Agile systems execution, and managing architectural debt across multi-year programs.