Technology Architect - AI

This position will work closely with IQT's government partners to understand their technology challenges and use their mastery of technology to investigate and recommend potential solutions from the venture-backed startup ecosystem, for both immediate mission needs and long term readiness. The Technology Architect team is responsible for identifying and executing investments in technology areas including (but not limited to) artificial intelligence, machine learning, enterprise search, AI automation, custom Large Language Models (LLMs), physical AI, and the security of AI systems.

This position will guide the technical aspects of an investment to ensure that IQT delivers effective and compelling solutions to our Intelligence Community/Department of Defense government partners.

• Identifies new AI/ML technology areas for investment and provides technical diligence on multiple complementary investments in that technology area.
• Develop and negotiation of Statements of Work contracts between IQT and start-up companies.
• Provides thought leadership in one or more areas of AI/ML subject matter expertise.
• Leverages technical/market experience and understanding of government partner use cases to identify new topics for, and oversee development of, technical engagement strategies across short- and long-term development horizons.
• Provide technical oversight on work programs; perform assessments of work program deliverables. Identifies and communicates paths for resolution of issues on work programs to key stakeholders and IQT Leadership.
• Oversee development of technology architectures and solutions to address AI-related problems faced by IQT's government partners.
• Coordinates with customers on AI technology adoption strategy in development of multiple work programs in a government partner mission area.

Minimal Qualifications

• Due to contractual reasons, U.S. Citizenship is required.
• Candidates with active top-secret clearance preferred; at a minimum, candidate must meet eligibility requirements for access to classified information.
• Advanced degree in a science or engineering discipline (or equivalent experience) preferred.
• Minimum 5 years professional experience with some portion of that time overseeing a technology development effort in a business and/or intelligence environment.
• Demonstrated expertise in artificial intelligence or machine learning.
• Previous experience negotiating milestone-based Statements of Work contracts.

Preferred Qualifications

• Demonstrated e xpertise in applied AI/ML, generative and multimodal AI, enterprise search, agentic systems, workflow automation, NLP, or production AI engineering :
  • Experience designing, evaluating, deploying, or securing LLMs, multimodal models, RAG systems, AI agents, or human-AI decision-support tools.
  • Knowledge of AI-enabled threat intelligence and cyber defense, including detection, vulnerability prioritization, incident investigation, and response.
  • Understanding of AI security across data, models, prompts, retrieval, memory, tools, APIs, infrastructure, and third-party components.
  • Familiarity with AI threats such as prompt injection, jailbreaks, unsafe tool use, data or model poisoning, evasion, model extraction, and sensitive-data leakage.
  • Knowledge of agent security, including identity, authorization, least privilege, sandboxing, containment, MCP security, and supply-chain integrity.
  • Experience with AI evaluation, assurance, and red teaming, including threat modeling, adversarial testing, safety evaluations, monitoring, and incident response.
  • Knowledge of AI-enabled security operations, including threat hunting, vulnerability analysis, malware analysis, digital forensics, alert triage, and detection engineering.
  • Familiarity with privacy-preserving AI, including federated learning, differential privacy, secure aggregation, confidential computing, and multiparty computation.
  • Understanding of explainable and auditable AI, including interpretability, uncertainty, evidence traceability, and decision provenance.
  • Experience using generative AI, world models, digital twins, or multi-agent simulations for red teaming, mission rehearsal, synthetic data, and defensive testing.
  • Familiarity with embodied AI, including vision-language-action models, spatial reasoning, robotics, autonomous systems, reinforcement learning, and human-machine teaming.
  • Working knowledge of modern AI architectures, including Transformers, mixture-of-experts, diffusion, state-space, retrieval, multimodal, agentic, and edge AI systems.
  • Understanding of the AI lifecycle, including data preparation, training, adaptation, evaluation, deployment, monitoring, governance, and retirement.
  • Experience with MLOps , LLMOps , or Agent Ops practices and supporting production infrastructure.
  • Hands-on…