AI/Cloud Engineer/Architect at Careers Decisionpointcorp, Chicago, IL
AI/Cloud Engineer/Architect at Careers Decisionpointcorp in Chicago, IL. Full-time. Est. up to $130k. Apply with swipeandhired.
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. <span data-ccp-props="{}" style="margin: 0px; padding: 0px; font-size: 10pt; font-family: Verdana, Verdana_EmbeddedFont, Verdana_MSFontService, sans-ser