AI Architect

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.