Intelligent Automation/AI-ML Engineer (Full Stack Engineer)Location: Herndon, VA (Hybrid Work)This position supports a U.S. Federal Government project. U.S. citizenship is required to meet the project’s eligibility and security clearance requirements.Experience: 8+ yrs

Position Summary

The Intelligent Application / AI-ML Engineer is the architect and guardian of intelligent application solutions that incorporate AI/ML technologies using tools such as open source AI models. This role ensures the operational efficiency and continuous refinement of integrated AI/ML solutions across the organization. Responsibilities span the design, maintenance, and optimization of intelligent applications, including troubleshooting and resolving issues that arise after implementation.

The engineer will also explore new ways to embed artificial intelligence and machine learning into applications, supporting continuous improvement, learning, and augmented decision making. A key focus is building repeatable, reusable intelligent solutions for document processing, data cleansing, and similar use cases.

Key Responsibilities

Solution Design and Development

Design, build, and deploy intelligent application solutions that integrate AI/ML models with business workflows using modern development frameworks and open source AI models.
Develop repeatable, reusable components and services for document processing, data extraction, classification, and data cleansing.
Translate business requirements into technical designs, solution architectures, and documented standards.

Build end-to-end applications as a full stack engineer, including front-end user interfaces, backend servers and APIs, AI/ML models, databases, and business rules.
Develop and expose AI capabilities and tools through Model Context Protocol (MCP) servers so applications can use them in a consistent, reusable way.
Implement business rules and decision logic that work alongside AI models, with clear governance, testing, and auditability.

AI/ML Operations and Optimization

Manage the lifecycle of open source AI models, including deployment, versioning, monitoring, retraining, and retirement.
Monitor model performance, accuracy, and drift, and tune solutions to maintain operational efficiency over time.
Implement feedback loops (e.g., human-in-the-loop validation) to drive continuous learning and refinement.
Optimize application performance, scalability, and resource utilization.

Support and Troubleshooting

Troubleshoot and resolve post-implementation issues in open source AI models, ML pipelines, and integrated applications.
Perform root cause analysis on production incidents and implement lasting fixes.