Principal AI Architect

Applied Research Solutions•Bedford, MA

1d•Onsite

About The Position

Applied Research Solutions is seeking a full-time Principal AI Architect for Hanscom AFB. The Principal AI Architect is a senior technical leader responsible for defining and guiding the architecture, engineering strategy, integration, and deployment of advanced artificial intelligence capabilities supporting U.S. Department of Defense (DoD) missions. This role requires employee pass background screening for SAP. The Automated Classification Management Environment (ACME) is a program initiated by the Air Force to modernize the classification management system. The current manual classification infrastructure limits the Department of War's ability to share data efficiently, which is crucial for Joint All Domain Command and Control (JADC2) operations. The ACME program aims to manage the lifecycle of classification and declassification in accordance with Executive Order 13526 and relevant policies, transitioning from a fragmented manual system to an AI-driven automated solution. The ACME program will involve several key tasks: Transitioning from a manual classification framework to an AI-driven automated enterprise solution. Managing the lifecycle of classified national security information, including original classification, declassification, and releasability processes. Addressing challenges such as financial strain due to lack of interoperability, static guidance leading to errors, a backlog of records awaiting review, and high error rates in derivative classifications. Implementing machine-readable data infrastructure and AI tools for automation across various classification levels (Unclassified, CUI, Confidential, Secret, Top Secret, SCI). Engaging in agile development methodologies for rapid prototyping and integration of new capabilities. This role provides enterprise-level technical leadership across AI/ML, generative AI, cloud, data, cybersecurity, software engineering, and mission systems. The Principal AI Architect translates complex mission requirements into secure, scalable, resilient, and interoperable AI architectures and establishes technical patterns that can be adopted across programs and operational environments. The successful candidate will serve as a trusted technical advisor to government customers, senior program leadership, engineering organizations, and mission stakeholders, while helping establish the technical vision and roadmap for next-generation AI capabilities.

Requirements

Must be a US citizen
Top Secret Security Clearance with SCI eligibility
Must pass SAP Screening
Bachelor's degree in computer science, Computer Engineering, Artificial Intelligence, Data Science, Systems Engineering or related technical discipline
15+ years of experience in software engineering, systems engineering, cloud architecture, AI/ML, data architecture, or related fields
5+ years of experience designing and implementing enterprise-scale AI/ML solutions
Demonstrated experience serving as a technical architect or senior technical leader for complex systems
Extensive experience with AI/ML architectures and production AI systems
Strong understanding of generative AI, LLMs, RAG, foundation models, model evaluation, and AI agents
Strong understanding of distributed systems, cloud-native architectures, APIs, microservices, containers, and Kubernetes
Extensive experience with MLOps, DevSecOps, CI/CD, and AI/ML lifecycle management
Strong understanding of cybersecurity architecture and secure software development
Demonstrated ability to communicate complex architecture and technology concepts to senior technical and government stakeholders

Nice To Haves

Master's degree in relevant technical discipline preferred

Responsibilities

Define enterprise and mission-level architectures for AI, machine learning, generative AI, and intelligent decision-support capabilities.
Establish technical vision, architecture principles, reference architectures, design patterns, and technology roadmaps for DoD AI programs.
Lead architecture decisions spanning AI models, data platforms, cloud infrastructure, applications, APIs, cybersecurity, networking, and edge computing.
Evaluate emerging AI technologies and determine their applicability to operational and mission requirements.
Provide technical leadership across the complete AI lifecycle - from experimentation and prototyping through production, deployment, and sustainment.
Establish reusable architecture patterns that enable secure and scalable adoption of AI across multiple mission areas.
Lead technical architecture reviews and provide recommendations to senior government and program leadership.
Architect enterprise-grade solutions leveraging large language models (LLMs), foundation models, multimodal AI, retrieval-augmented generation (RAG), AI agents, and domain-specific models.
Design secure architectures for government use of commercial, open-source, and internally developed foundation models.
Establish approaches for model selection, fine-tuning, prompt engineering, evaluation, model serving, inference optimization, and lifecycle management.
Design AI agent architectures incorporating tools, APIs, knowledge bases, workflow orchestration, human oversight, and appropriate safeguards.
Establish methodologies for evaluating model accuracy, robustness, security, reliability, bias, hallucination, and mission suitability.
Develop architectures for AI-enabled decision support while maintaining appropriate human oversight and accountability.
Design AI architectures across cloud, hybrid, on-premises, disconnected, tactical, and edge environments.
Architect GPU/accelerated computing infrastructure for AI training and inference workloads.
Develop strategies for deploying AI capabilities in constrained or intermittently connected operational environments.
Integrate AI services with existing DoD applications, enterprise platforms, mission systems, data environments, and APIs.
Define architectures supporting high availability, resilience, scalability, fault tolerance, and mission continuity.
Establish enterprise MLOps and AI engineering practices supporting repeatable model development, testing, deployment, monitoring, and lifecycle management.
Define CI/CD and DevSecOps patterns for AI/ML systems.
Establish model registries, feature stores, data pipelines, evaluation frameworks, observability, and model monitoring architectures.
Design automated mechanisms for model validation, security testing, performance monitoring, drift detection, and rollback.
Ensure AI systems can be maintained, upgraded, and governed throughout their operational lifecycle.
Integrate cybersecurity requirements into AI architectures from initial design through deployment and sustainment.
Apply Zero Trust principles to AI, data, application, identity, and infrastructure architectures.
Support Risk Management Framework (RMF), Authority to Operate (ATO), security control implementation, continuous monitoring, and related federal/DoD processes.
Design architectures addressing data protection, identity and access management, encryption, auditability, model security, supply-chain security, and secure software development.
Establish controls for AI model integrity, data provenance, access control, prompt injection, data leakage, adversarial inputs, and other AI-specific security risks.
Incorporate responsible AI principles, explainability, traceability, human oversight, and appropriate governance into system architecture.
Partner with cybersecurity and compliance teams to ensure AI solutions satisfy applicable DoD and federal requirements.
Define architectures for collecting, integrating, managing, governing, and securing AI-ready data.
Establish data strategies supporting structured, unstructured, geospatial, sensor, text, image, video, and other mission data.
Design data pipelines and knowledge architectures supporting RAG, model training, analytics, and AI inference.
Establish data governance, metadata, lineage, quality, access, and provenance requirements.
Address data interoperability across enterprise, mission, coalition, and edge environments where applicable.
Act as the senior technical authority for AI architecture within assigned programs.
Engage directly with senior government executives, Chief Architects, Chief Technology Officers, Chief Data/AI Officers, program managers, mission owners, and engineering leadership.
Translate strategic mission objectives into executable technical architectures and implementation roadmaps.
Lead technical working groups involving government personnel, contractors, vendors, and technology partners.
Assess build-versus-buy decisions and evaluate commercial, government, and open-source AI technologies.
Provide technical guidance for acquisition strategies, technical requirements, Statements of Work (SOWs), Requests for Proposals (RFPs), and vendor evaluations.
Mentor senior engineers, architects, data scientists, and AI practitioners.
Other duties as assigned.