AI Developer Engineer

Unissant•Ashburn, VA

10d•Remote

About The Position

Unissant, Inc. is seeking an AI Developer/Engineer to join their team and support their client, the Federal Emergency Management Agency (FEMA). This is a remote position. The ideal candidate is a hands-on AI/ML engineer who can develop, evaluate, tune, and operationalize secure and maintainable AI capabilities for FEMA mission programs. This individual will deliver solutions across Azure Ready AI and other FEMA-approved platforms, including AWS, GCP, Microsoft 365, ServiceNow, and CRM/SaaS environments.

Requirements

Five (5) to ten (10) years of experience in AI/ML, data science, software engineering, cloud application development, or related technical disciplines.
Proficiency in Python, R, or JavaScript, with experience using machine-learning and deep-learning frameworks.
Experience with Terraform, CI/CD pipelines, git-based source control, and observability tooling.
Experience analyzing complex datasets and deploying scalable, maintainable AI/ML solutions into production.
Experience with cloud-hosted AI services, secure APIs, data pipelines, and enterprise application integration.
Strong analytical, technical documentation, troubleshooting, and collaboration skills.
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field required.
U.S. Citizenship is required.
Favorably adjudicated FEMA Public Trust suitability is required before the employee's start date.
Must meet DHS/FEMA training and handling requirements for SBU/FOUO and potentially CUI, PII, or other sensitive FEMA data.

Nice To Haves

Cloud, AI/ML, data-science, DevSecOps, or relevant technical certification preferred.

Responsibilities

Design, develop, and productionize RAG applications, chatbots, document translation and summarization services, predictive models, anomaly-detection capabilities, and routing/optimization solutions.
Evaluate new AI use cases and provide feasibility, architecture, cost, complexity, schedule, and solution-stack estimates.
Select appropriate algorithms; prepare and engineer data; train, validate, tune, document, and monitor models.
Define and maintain model-performance metrics, evaluation processes, monitoring, and improvement approaches.
Integrate AI/ML models with full-stack applications and enterprise workflows through APIs, approved cloud AI services, and secure data pipelines.
Implement MLOps and DevSecOps practices, including git-based workflows, automated testing, CI/CD, infrastructure-as-code, observability, and controlled deployments.
Support AI security boundaries, ATO artifacts, data governance, role-based access controls, and model/application risk-management activities.
Create technical documentation covering architecture, experiments, model performance, operations, and customer use.