VTG is seeking an AI Architect to design, develop, evaluate, and deploy advanced artificial intelligence solutions. This role involves leading the end-to-end AI lifecycle, establishing AI governance frameworks, and serving as a senior technical advisor on AI strategy for mission-critical initiatives. The ideal candidate will have deep expertise in modern AI/ML architectures, including agentic AI systems and LLMs, with hands-on experience in building and operationalizing AI systems at scale.
About the role
Overview VTG is seeking a highly experienced and innovative AI Architect to lead the design, development, evaluation, and deployment of advanced artificial intelligence solutions in support of mission-critical and enterprise initiatives. This role requires deep expertise in modern AI/ML architectures, including agentic AI systems, large language models (LLMs), autonomous workflows, AI evaluation frameworks, and production-grade machine learning operations (MLOps). This position is located in Chantilly, VA. The ideal candidate is both technically exceptional and customer-facing — capable of advising senior leadership, engaging directly with government and commercial stakeholders, and serving as a trusted authority on emerging AI technologies and best practices. This individual must have hands-on experience building and operationalizing AI systems at scale and possess a strong understanding of modern AI governance, responsible AI principles, and evaluation methodologies. What will you do? Architect, Design, And Implement Advanced AI/ML Solutions, Including Agentic AI systems Retrieval-Augmented Generation (RAG) Large Language Model (LLM) integrations Autonomous and semi-autonomous workflows AI orchestration frameworks Predictive analytics and traditional ML models Lead The End-to-end AI Lifecycle, Including Data ingestion and preparation Model development and fine-tuning AI testing and evaluation Model deployment and monitoring Operational sustainment and optimization Develop And Mature AI Evaluation And Testing Methodologies, Including Traditional ML evaluation metrics LLM benchmarking Red teaming and adversarial testing Hallucination detection Bias and fairness assessments Performance and reliability testing Human-in-the-loop evaluation strategies Design scalable MLOps and AIOps pipelines to support secure and repeatable deployment of AI capabilities in enterprise and cloud environments Establish And Implement AI Governance Frameworks, Including Responsible AI practices Security and compliance controls Model transparency and explainability Risk management Data governance standards Serve as a senior technical advisor to customers, executives, and program leadership on AI strategy, architecture, modernization, and emerging capabilities. Lead technical discussions, architecture reviews, demonstrations, and customer briefings with confidence and authority. Stay current with emerging AI research, industry trends, open-source technologies, and commercial AI platforms; continuously assess applicability to organizational and customer needs. Mentor engineers, data scientists, and software developers on AI best practices, architectures, and implementation strategies. Collaborate across engineering, cybersecurity, cloud, data, and product teams to deliver integrated AI solutions. Do you have what it takes? Required Qualifications Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Mathematics, or related technical field. Master’s degree or PhD preferred. 10–15+ years of experience in artificial intelligence, machine learning, software engineering, data engineering, or related technical disciplines. Demonstrated experience architecting and deploying enterprise-scale AI/ML solutions in production environments. Hands-on experience building and operationalizing: Agentic AI systems LLM-powered applications AI orchestration frameworks Autonomous decision-support systems Strong understanding of: Machine learning algorithms Deep learning techniques Natural language processing (NLP) Reinforcement learning concepts Statistical modeling and AI evaluation methodologies Experience with AI testing, validation, benchmarking, and evaluation frameworks for both traditional ML and generative AI systems. Experience implementing practical MLOps pipelines and AI operationalization frameworks. Strong programming experience with: Python Jupyter Notebooks or equivalent notebook environments Experience with big data and distributed processing technologies such as: Apache Spark Databricks (preferred) Experience with one or more major cloud platforms: Microsoft Azure Amazon Web Services (AWS) Google Cloud Platform (GCP) Familiarity with: Vector databases AI orchestration frameworks (LangChain, Semantic Kernel, CrewAI, AutoGen, etc.) Containerization and orchestration technologies CI/CD pipelines for AI deployments Strong communication and presentation skills with demonstrated customer-facing experience. Ability to translate complex technical concepts into actionable business and mission solutions. Preferred Qualifications Experience supporting Federal Government, DoD, Intelligence Community, or highly regulated environments. Experience implementing secure AI architectures in classified or sensitive environments. Familiarity with AI security, adversarial AI, and zero trust principles. Experience with GPU infrastructure, model optimization, and scalable inference architectures. Published research, conference presentations, patents, or contributions to the AI community preferred. Active participation in AI research communities, industry working groups, or open-source AI initiatives. Clearance Requirement Active Secret security clearance required, or ability to obtain and maintain a Secret clearance. Desired Characteristics Strategic thinker with strong technical depth and hands-on engineering capability. Passion for continuous learning and staying ahead of rapidly evolving AI technologies. Comfortable operating in ambiguous and fast-paced technical environments. Strong leadership, collaboration, and mentoring abilities. Customer-focused with executive presence and consultative communication skills. Technologies & Tools Experience With Several Of The Following Is Desired Python Jupyter Notebook Apache Spark Databricks TensorFlow PyTorch Hugging Face LangChain Semantic Kernel CrewAI AutoGen Kubernetes Docker Azure AI Services AWS SageMaker Google Vertex AI Vector databases MLflow GitLab/GitHub CI/CD pipelines Work Environment This role may support hybrid, on-site, or customer-location work environments depending on program requirements. Occasional travel may be required for customer engagement, technical workshops, or industry events.