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.
Responsibilities
- 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
- Data ingestion and preparation
- Model development and fine-tuning
- AI testing and evaluation
- Model deployment and monitoring
- Operational sustainment and optimization
- 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
- 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.
Basic 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
- 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
- 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
- Jupyter Notebooks or equivalent notebook environments
- Experience with big data and distributed processing technologies such as:
- + Apache Spark Databricks (preferred)
- Databricks (preferred)
- Experience with one or more major cloud platforms:
- + Microsoft Azure Amazon Web Services (AWS) Google Cloud Platform (GCP)
- 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
- 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.
- Active Secret security clearance required, or ability to obtain and maintain a Secret clearance.
- 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.
- 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
Tags & focus areas
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