Architect

DecisionPoint | Cortek•Chicago, IL

2d•Remote

About The Position

DecisionPoint seeks a senior-level, part-time AI/Cloud Engineer/Architect to design, build, integrate, and secure artificial intelligence capabilities supporting the USMEPCOM ENLIST program. The role develops knowledge assistants, retrieval workflows, and bounded multi-step agents for Government operations, with attention to security, traceability, tool restrictions, and human oversight. The position combines cloud architecture, secure application engineering, API integration, retrieval-based AI, and agentic workflow expertise. The architect translates mission needs into secure, scalable, supportable AI solutions integrated with existing applications, data sources, cloud services, and operational workflows. The role works closely with mission owners, software teams, data professionals, cybersecurity personnel, testers, and infrastructure teams. This is a remote, part-time position supporting approximately 0.5 full-time-equivalent effort. Chicago-area candidates are preferred. Applicants acknowledge and consent to having their resume included in an active competitive government contract bid.

Requirements

Active Secret clearance or higher.
Bachelor’s degree in computer science, software engineering, information technology, or a related field, or a permitted equivalent.
Minimum of 10 years of relevant professional experience.
Experience designing or implementing cloud architectures.
Strong hands-on experience with Python or other approved application-development languages.
Experience designing, developing, securing, and integrating APIs.
Experience designing or implementing retrieval workflows.
Experience with secure application integration.
Experience designing or implementing AI agents or multi-step AI workflows.
Ability to build knowledge assistants, retrieval-enabled applications, and bounded agentic capabilities.
Experience implementing application authentication and authorization controls.
Experience developing automated tests for applications, APIs, integrations, or AI workflows.
Ability to implement source citations, tool permissions, exception handling, audit logging, and human approval steps.
Understanding of cloud security, application security, identity and access management, data protection, and secure integration patterns.
Ability to translate mission and operational requirements into secure, scalable, testable, and maintainable technical solutions.
Ability to prepare and maintain architecture diagrams, interface documentation, technical designs, and operating guidance.
Strong written and verbal communication skills, including the ability to explain technical architecture, risks, and recommendations to technical and nontechnical stakeholders.

Nice To Haves

Relevant cloud architecture certification, such as AWS Certified Solutions Architect, Microsoft Certified: Azure Solutions Architect Expert, Google Professional Cloud Architect, or a comparable credential.
CISSP or CSSLP certification aligned with DoD 8570 IASAE Level II or applicable successor qualification requirements.
Experience supporting federal or Department of Defense programs.
Experience developing retrieval-augmented generation applications.
Experience with vector databases, semantic search, embeddings, document processing, or enterprise search platforms.
Experience with AI agent frameworks, orchestration platforms, or tool-calling architectures.
Experience implementing responsible AI controls, output validation, model monitoring, drift detection, or safeguards against unsupported responses.
Experience with cloud-native services, containers, Kubernetes, serverless architectures, or managed AI platforms.
Experience with infrastructure as code, continuous integration and delivery, automated deployment, observability, and rollback automation.
Experience working with controlled unclassified information or systems operating in regulated cloud environments.
Familiarity with DoD Impact Level 4 or comparable Government cloud-security requirements.
Experience integrating AI capabilities with enterprise identity, data warehouse, business intelligence, knowledge management, or workflow platforms.

Responsibilities

Design and build secure AI-enabled knowledge assistants for mission and operational use cases.
Develop retrieval workflows connected to approved AI capabilities, authoritative enterprise content, and data sources.
Design bounded multi-step AI agents with defined tools, permissions, operating limits, escalation paths, and human approval requirements.
Develop AI applications and supporting services using Python or other Government-approved application languages.
Design and implement secure APIs, microservices, application interfaces, and integration patterns.
Implement authentication, authorization, identity propagation, and role-based access controls for AI-enabled applications.
Establish tool-level permissions and safeguards restricting AI agents to approved systems, data, functions, and actions.
Implement source citations and traceable references for AI-generated responses.
Develop grounding, retrieval, and validation mechanisms to improve the accuracy and reliability of AI-generated content.
Incorporate human-review and approval checkpoints into workflows involving sensitive, consequential, or externally executed actions.
Implement exception handling, retry controls, timeout behavior, escalation paths, and safe failure modes.
Create audit logs covering user activity, data access, retrieval results, agent actions, tool invocations, approvals, exceptions, and relevant system outcomes.
Develop automated unit, integration, API, regression, security, and workflow tests for AI-enabled capabilities.
Validate that knowledge assistants, retrieval workflows, and agents meet functional, security, performance, traceability, and usability requirements.
Design secure cloud architectures for AI applications, data pipelines, retrieval services, model access, application integration, monitoring, and operational support.
Integrate AI capabilities with approved cloud platforms, applications, databases, data warehouses, identity services, APIs, and business workflows.
Support secure data pipelines and AI-ready data architectures for retrieval, analysis, automation, and decision support.
Collaborate with data teams to ensure AI and retrieval workflows use approved, accurate, governed, and appropriately secured data.
Support monitoring of retrieval quality, application behavior, model performance, model drift, security events, and operational exceptions.
Establish technical patterns for prompt management, retrieval configuration, agent orchestration, version control, testing, deployment, and rollback.
Evaluate cloud and AI architecture alternatives and document technical decisions, constraints, risks, dependencies, and recommendations.
Develop architecture diagrams, interface specifications, data-flow diagrams, security documentation, operating procedures, and technical implementation guidance.
Coordinate with cybersecurity personnel on application security, cloud security, data protection, authorization, vulnerability remediation, and inspection readiness.
Support code and architecture reviews and provide technical guidance to developers, integration engineers, testers, and operational teams.
Assist with troubleshooting AI application, retrieval, API, authentication, data-access, and cloud-integration failures.
Support release readiness, implementation, technical validation, operational handoff, and knowledge transfer.