AI Full-Stack Developer

Abacus Technology•Arlington, VA

About The Position

Abacus Technology is seeking an AI Full-Stack Developer to design, configure, develop, and implement secure, responsible, and scalable enterprise AI solutions for the U.S. Postal Service (USPS) Office of Inspector General (OIG). This is a full-time position.

Requirements

5 years experience in software development including at least 2 years with relevant AI tools and technologies.
Bachelor’s degree in computer science, software engineering, data science, information systems, or a related field. Additional experience may be substituted for degree requirements.
Must have hands-on experience with AI/ML development and operations within an AI framework or platform, embedding bias mitigation, explainability, and fairness-assessment controls into software-development and operational workflows.
Demonstrated experience configuring, developing, or integrating AI and ML tools.
Experience creating role-based technical playbooks and operational guidance.
Experience developing generative AI or large language model applications.
Hands-on experience with Databricks or similar tool for data engineering, analytics, machine learning, or AI development.
Experience with Collibra or a comparable enterprise data-governance platform.
Proficient in Python and at least one additional language, such as Java, JavaScript, TypeScript, or C#.
Experience with a modern front-end framework such as React, Angular, or Vue.
Knowledge of SQL, data modeling, data pipelines, relational databases, and non-relational databases.
Strong analytical, troubleshooting, documentation, and communication skills.
Must be a US citizen.

Nice To Haves

Databricks Data Engineer, Machine Learning, or Generative AI certification desired.
Experience integrating Collibra with Databricks Unity Catalog preferred.
Experience with MLflow, LangChain, LlamaIndex, semantic search, vector databases, and model-serving platforms desired.
Experience implementing bias detection, bias mitigation, model explainability, or fairness-assessment processes.
Experience with explainability and fairness tools such as SHAP, LIME, Fairlearn, AI Fairness 360, or comparable technologies preferred.
Knowledge of responsible AI and AI risk-management frameworks, including NIST AI RMF and ISO/IEC 42001 desired.

Responsibilities

Work collaboratively with government subject matter experts, users, and teammates to identify, design, develop, and deliver AI governance and management framework configuration, workflows and applications.
Configure, customize, and integrate enterprise AI tools and platforms.
Configure and extend Collibra capabilities supporting data cataloging, business glossaries, data lineage, stewardship, data quality, policies, and governance workflows.
Use Collibra to document ownership, lineage, policies, controls, risk classifications, and governance evidence for AI data and models.
Develop AI-enabled capabilities such as intelligent search, knowledge assistants, document analysis, summarization, classification, recommendation, and workflow automation.
Build and maintain front-end applications, back-end services, APIs, databases, and cloud integrations incorporating AI/ML capabilities.
Create dashboards and reports to provide timely visibility into the performance, reliability, risk, and operational health of production AI systems.
Develop data pipelines and AI/ML workloads using Databricks, including notebooks, workflows, Delta Lake, Unity Catalog, MLflow, and model-serving capabilities.
Support AI inventories, use-case registration, risk classification, approval workflows, ongoing monitoring, and auditability.
Develop role-based responsible AI playbooks for developers, data scientists, product owners, business users, governance teams, risk managers, system administrators, and executive decision-makers.
Create reusable templates for AI use-case intake, risk classification, model documentation, data documentation, bias testing, fairness assessments, explainability reviews, security reviews, release approvals, and post-deployment monitoring.
Implement reusable workflow templates within GitHub, Azure DevOps, Jira, Confluence, Collibra, Databricks, and the broader Atlassian environment.