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

Node.Digital•Herndon, VA

1d•Hybrid

About The Position

The Intelligent Application / AI-ML Engineer designs, builds, deploys, and maintains intelligent application solutions that incorporate AI/ML technologies, including open-source AI models. The role ensures the operational efficiency and continuous refinement of integrated AI/ML solutions, and includes troubleshooting and resolving post-implementation issues. The engineer explores ways to embed AI and machine learning into applications to support continuous improvement, learning, and augmented decision-making, with a focus on reusable solutions for document processing, data extraction, classification, and data cleansing. The position combines full-stack application engineering with AI/ML operations. It involves creating front-end interfaces, backend services and APIs, AI/ML models, databases, and business rules; exposing AI capabilities through Model Context Protocol (MCP) servers; and managing model deployment, versioning, monitoring, retraining, and retirement. The engineer monitors model performance and drift, implements feedback loops such as human-in-the-loop validation, optimizes application performance, and supports production systems in partnership with infrastructure, security, and platform teams. The engineer collaborates with business analysts, process owners, data teams, and IT to deliver solutions aligned with business goals, and communicates progress, risks, and results to both technical and non-technical audiences. The role is located in Herndon, Virginia, with hybrid work.

Requirements

Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, or a related field, or equivalent 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.
Eight or more years of experience.

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 cloud platforms such as 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.
U.S. citizenship is preferred.

Responsibilities

Design, build, and deploy intelligent application solutions integrating AI/ML models with business workflows, 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.
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, such as 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 for 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.