Intelligent Automation/AI-ML Engineer (Full Stack Engineer)

Node.Digital•Herndon, VA

6h•Hybrid

About The Position

The Intelligent Application / AI-ML Engineer designs, builds, deploys, and supports intelligent application solutions that integrate AI/ML technologies, including open-source AI models, with business workflows. The role focuses on operational efficiency and continuous refinement of AI/ML solutions, including reusable capabilities for document processing, data extraction, classification, and data cleansing. The engineer develops end-to-end applications across front-end interfaces, backend services and APIs, AI/ML models, databases, and business rules, and exposes AI capabilities through Model Context Protocol (MCP) servers. The position manages open-source model lifecycles, monitors performance and drift, tunes solutions, and implements feedback loops such as human-in-the-loop validation. It also troubleshoots production issues, performs root cause analysis, maintains support documentation, and collaborates with infrastructure, security, platform, business, data, and IT teams. This role supports a U.S. Federal Government project in Herndon, Virginia, with hybrid work. U.S. citizenship is required to meet project eligibility and security clearance requirements.

Requirements

Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, or a related field, or equivalent experience.
8+ years of experience.
Experience in intelligent application development, including hands-on enterprise application development.
Working experience with open-source AI models and document understanding techniques, or comparable AI/ML frameworks and tools.
Proficiency in Python and common ML libraries such as scikit-learn, pandas, TensorFlow, or PyTorch.
Understanding of the ML lifecycle, including data preparation, training, evaluation, deployment, monitoring, and retraining.
Experience building document processing and data cleansing solutions.
Strong troubleshooting and problem-solving skills in production environments.
Knowledge of APIs, integrations, SQL, and data formats such as JSON, XML, and CSV.
Excellent written and verbal communication skills.
Full-stack development experience, including Python and Java for backend services and APIs and Angular for front-end user interfaces.
Hands-on experience with MCP, including building or integrating MCP servers and tools.
Experience designing and working with relational and/or NoSQL databases, including schema design and query optimization.
U.S. citizenship is required for project eligibility and security clearance requirements.

Nice To Haves

Cloud, AI/ML, or software development certifications, such as advanced developer or solution architect credentials.
Experience with OCR, NLP, computer vision, or generative AI/LLM integration.
Familiarity with Azure, AWS, or GCP and MLOps practices.
Knowledge of Agile delivery, CI/CD, and source control such as Git.
Understanding of data governance, security, and responsible AI practices.

Responsibilities

Design, build, and deploy intelligent application solutions integrating AI/ML models with business workflows using modern development frameworks and open-source AI models.
Develop 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, including front-end interfaces, backend servers and APIs, AI/ML models, databases, and business rules.
Develop and expose AI capabilities and tools through MCP servers for consistent, reusable application access.
Implement business rules and decision logic alongside AI models with governance, testing, and auditability.
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.
Implement feedback loops, including human-in-the-loop validation, to support continuous learning and refinement.
Optimize application performance, scalability, and resource utilization.
Troubleshoot and resolve post-implementation issues in AI models, ML pipelines, and integrated applications.
Perform root cause analysis on production incidents and implement lasting fixes.
Maintain runbooks, support documentation, and knowledge base articles.
Partner with infrastructure, security, and platform teams to maintain a stable and compliant environment.
Collaborate with business analysts, process owners, data teams, and IT to deliver solutions aligned with business goals.
Communicate progress, risks, and results to technical and non-technical audiences.