AI/Cloud Engineer/Architect

About this role

Overview
DecisionPoint seeks a part-time AI/Cloud Engineer/Architect to design, build, integrate, and secure artificial intelligence capabilities supporting the USMEPCOM ENLIST program. This senior-level position will develop knowledge assistants, retrieval workflows, and bounded multi-step agents that enhance Government operations while maintaining appropriate security, traceability, tool restrictions, and human oversight.
The AI/Cloud Engineer/Architect will combine cloud architecture, secure application engineering, API integration, retrieval-based AI, and agentic workflow expertise. The role will implement source citations, authentication and authorization controls, tool permissions, exception handling, audit logging, automated testing, and human approval checkpoints.
The successful candidate must be able to translate mission needs into secure, scalable, and supportable AI solutions that integrate with existing applications, data sources, cloud services, and operational workflows. The architect will work 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.
Note: By applying to this position, you acknowledge and consent to having your resume included in an active competitive government contract bid.
Duties & Responsibilities
The AI/Cloud Engineer/Architect will:

• Design and build secure AI-enabled knowledge assistants for mission and operational use cases.

• Develop retrieval workflows that connect approved AI capabilities with authoritative enterprise content and data sources.

• Design bounded multi-step AI agents with explicitly 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 that limit AI agents to approved systems, data, functions, and actions.

• Implement source citations and traceable references so users can identify the authoritative information supporting AI-generated responses.

• Develop grounding, retrieval, and validation mechanisms that 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 that capture 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 supporting 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 used for retrieval, analysis, automation, and decision support.

• Collaborate with data teams to ensure retrieval and AI workflows use approved, accurate, governed, and appropriately secured data.

• Support monitoring for 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 to address application security, cloud security, data protection, authorization, vulnerability remediation, and inspection-readiness requirements.

• 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 activities.
Qualifications
Required Qualifications
Candidates must meet all the following 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…