Senior AI Systems Architect

Unissant•Ashburn, VA

23h•Onsite

About The Position

Unissant, Inc. delivers innovative capabilities to the agencies that keep our nation healthy and safe. We apply our domain expertise, data acumen, and technology know-how to achieve breakthrough results for our clients. Working collaboratively, we advance missions and careers through a focus on honesty, integrity, and dependability. We continuously look for talent, excited to join that effort. To learn more about our exciting organization, please visit us at www.unissant.com . We are seeking a Senior AI Systems Architect to serve as a technical lead and enterprise architect for Artificial Intelligence (AI), Machine Learning (ML), Generative AI (Gen-AI), and advanced analytics initiatives supporting our federal customer. This role is responsible for designing, operationalizing, and scaling secure, mission-focused AI capabilities that enhance customer's AI/ML services, image analysis, anomaly detection, operational intelligence, and decision-support missions. Position Overview: The Senior AI Systems Architect leads the design of enterprise AI ecosystems and reusable AI services within the client's AI Enablement Center, enabling AI-as-a-Service capabilities across multiple applications and operational environments. The role combines enterprise architecture, AI engineering, cloud modernization, cybersecurity, and mission operations expertise to deliver scalable, secure, and governable AI solutions aligned with operational requirements and federal AI governance mandates. This position serves as a trusted technical advisor to leadership, program stakeholders, architects, developers, data scientists, and cybersecurity teams while driving the transition of AI capabilities from research and experimentation into production operational environments.

Requirements

12+ years of experience in enterprise architecture, AI/ML engineering, cloud modernization, or advanced analytics solutions.
5+ years designing and implementing AI/ML or Gen-AI solutions in enterprise or federal environments.
Strong expertise in: Generative AI, LLM architectures, RAG frameworks, MLOps, AI operationalization, Computer Vision, NLP, Cloud-native architectures.
Experience with AWS and/or Google Cloud AI ecosystems.
Strong experience designing: Event-driven architectures, API ecosystems, Data streaming platforms, Microservices architectures, Enterprise integration frameworks.
Experience implementing secure AI and DevSecOps pipelines within regulated environments.
Strong understanding of federal cybersecurity and compliance frameworks including NIST RMF and Zero Trust.
Experience operationalizing AI-as-a-Service capabilities across mission applications.
Experience designing scalable multimodal AI platforms supporting image intelligence, semantic search, and automated metadata generation.
Experience reducing operational analytical processing times from hours/days to near real-time through the implementation of massively parallel AI architectures.
Experience implementing secure AI governance and operationalization frameworks aligned with customer AI policies and federal security mandates.
Experience enabling reusable enterprise AI services to accelerate adoption of AI across operational domains.
Bachelor's Degree in Computer Science, Information Technology Management or Engineering is required.
Must have excellent written and verbal communication skills.
Ability to convey technical information to non-technical individuals.
Demonstrated experience communicating effectively across internal and external organizations.
Must work well in a matrixed team environment.
United States Citizen.
Must have an active DHS clearance.

Nice To Haves

Experience supporting National Security or Intelligence Community programs.
Experience with: Image analysis and anomaly detection platforms, Operational intelligence environments.
Knowledge of: NIEM, WCO Data Model, HL7/FHIR, Federated data architectures, Synthetic data generation.
Experience operationalizing multimodal AI and large-scale AI inferencing environments.
MBA/Master's Degree in a relevant field of study preferred.
Any related systems engineering, or related technical certifications are desired.
SAFe Agile or cloud certifications preferred.

Responsibilities

Serve as the lead architect for AI and Gen-AI initiatives supporting operational mission requirements across national security customer domains.
Design and operationalize enterprise AI architectures supporting scalable AI-as-a-Service capabilities across client applications.
Develop reusable AI frameworks, shared services, and reference architectures aligned with customer enterprise standards and governance models.
Architect multimodal AI solutions supporting text, image, video, and structured/unstructured data analysis.
Define enterprise AI integration patterns supporting cloud-native, event-driven, and microservices-based architectures.
Lead design and deployment of AI/ML and Gen-AI solutions including: RAG architectures, Agentic AI frameworks, LLM orchestration, Computer Vision, Image analysis and object detection, Anomaly detection, Predictive analytics, NLP and summarization services, Intelligent search and semantic retrieval.
Design scalable pipelines for AI model training, inference, monitoring, retraining, and operational management.
Implement secure Gen-AI guardrails addressing: Hallucination mitigation, Prompt injection prevention, Prompt leakage controls, Explainability and traceability, Responsible AI governance.
Architect high-throughput AI processing frameworks capable of supporting near real-time mission operations and large-scale analytical workloads.
Integrate AI services into operational systems, mission workflows, and enterprise data platforms.
Architect event-driven data ecosystems leveraging Kafka/MSK, APIs, streaming pipelines, and real-time interoperability frameworks.
Support modernization of legacy systems and data platforms into scalable cloud-native AI-enabled architectures.
Design interoperable data frameworks leveraging NIEM, WCO, JSON/REST APIs, and other federal interoperability standards.
Enable AI integration across structured and unstructured mission datasets including operational intelligence, targeting data, image repositories, and external partner feeds.
Architect secure AI environments leveraging AWS and Google cloud ecosystems including: Amazon Bedrock, AWS SageMaker, Vertex AI, Databricks, Containerized AI platforms.
Implement Infrastructure as Code (IaC), automated provisioning, CI/CD, and MLOps pipelines supporting rapid AI deployment and lifecycle management.
Design AI operationalization pipelines with: Automated model deployment, Drift monitoring, Retraining triggers, Model governance, Security automation.
Collaborate with DevSecOps and cybersecurity teams to integrate AI solutions into secure enterprise delivery pipelines.
Ensure all AI architectures comply with: DHS security policies, NIST 800-series standards, RMF requirements, Zero Trust principles, Federal AI governance guidelines.
Architect secure data access, role-based controls, encryption, audit logging, and AI activity monitoring.
Support ATO processes and system security documentation for AI-enabled solutions.
Establish governance frameworks for AI model validation, testing, explainability, and operational accountability.
Collaborate with customer, cloud providers, academic institutions, and emerging technology partners to evaluate and operationalize innovative AI capabilities.
Lead technical evaluations, proof-of-concepts, and pilot initiatives for emerging AI technologies.
Research and operationalize advanced AI capabilities including: Agentic AI, Multimodal AI, Federated AI, Synthetic data generation, Massive parallel AI processing, AI-enabled image intelligence.
Support the expansion of CBP's Technology Reference Model (TRM) for AI technologies and tools.
Provide technical leadership and mentorship to AI engineers, data scientists, architects, and development teams.
Lead architecture reviews, technical strategy sessions, and solution design workshops.
Translate operational mission requirements into scalable technical architectures and implementation roadmaps.
Develop technical white papers, executive briefings, architecture diagrams, and implementation strategies for leadership.
Support proposal development, technical volume writing, and customer demonstrations for customer's AI initiatives.