AI Architect

VTG•Chantilly, VA

1d•Hybrid

About The Position

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.

Requirements

Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Mathematics, or related technical field.
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.
Active Secret security clearance required, or ability to obtain and maintain a Secret clearance.

Nice To Haves

Master’s degree or PhD preferred.
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.

Responsibilities

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.