AI/ML Engineer (Midlevel)

Node.Digital•Herndon, VA

19d•Hybrid

About The Position

Node.Digital is seeking an AI/ML Engineer to design, maintain, and optimize intelligent automation solutions incorporating generative AI and machine learning. The role focuses on modern generative AI engineering, including building applications that support continuous improvement, learning, and augmented decision-making. The engineer will build solutions using Amazon Bedrock, foundation models, and RAG architectures; develop repeatable document-processing and data-cleansing bots; deploy scalable AI/ML models on AWS; create API endpoints and integrations; and implement model evaluation, monitoring, and improvement processes. The position also includes troubleshooting AI Center and resolving issues after implementation, and collaborating with cross-functional teams to incorporate AI capabilities across business functions. The role is based in Herndon, Virginia, with hybrid work. U.S. citizenship is preferred. A bachelor's degree and at least five years of experience in automation engineering roles focused on AI/ML integrations and generative AI application development are specified. The ideal candidate communicates effectively in technical discussions, is comfortable with Agile methodologies and remote collaboration, adapts to different organizational structures, aligns with the customer's mission and values, and has strong data analysis, data architecture, and complex problem-solving skills.

Requirements

Bachelor's degree.
At least 5 years of experience in automation engineering roles focused on AI/ML integrations and generative AI application development.
At least 5 years of experience developing AI/ML-based applications or solutions, with a strong focus on generative AI applications.
Hands-on experience with AWS services including Amazon Bedrock, S3, SageMaker, CDK, Lambda, and other AI/ML services.
Experience with generative AI models and frameworks, including LLMs, RAG architectures, prompt engineering, and model fine-tuning.
Hands-on experience with OCR, ICR, and OMR technologies.
Good programming knowledge in Python and relevant AI/ML frameworks such as TensorFlow, PyTorch, and LangChain.
Understanding of document processing, classification, and data extraction.
Knowledge of NLP, deep learning, and generative AI.
Hands-on web application and API development experience.
Proficiency in asynchronous and multithreaded programming.
Knowledge of algorithms, data structures, complexity, optimization, caching, and security.
Experience with JSON, SOAP, REST, XML, XHTML, XSD, and XSLT.
Knowledge of object-oriented concepts and database concepts.
Experience with databases such as SQL Server and PostgreSQL.
Knowledge of AWS cloud architecture patterns and serverless computing.
Experience with CI/CD pipelines and DevSecOps practices.
Experience with a toolchain including TFS, SVN, and Git.
Involvement in different SDLC phases and working exposure to SDLCs such as Agile methodologies.
Effective communication skills for technical discussions.
Comfort with Agile methodologies and ability to work remotely.
Adaptability to varying organizational structures, alignment with the customer's mission and values, data analysis and data architecture skills, and strong problem-solving abilities for complex AI/ML challenges.

Nice To Haves

Experience with NoSQL databases and vector databases for RAG implementations.
Knowledge of Agile methodologies is desirable.
Experience with front-end frameworks such as React, Angular, or Vue.js, and modern web development.
UiPath RPA Developer Certification and UiPath AI Center experience.
Knowledge of chatbot development and conversational AI.
Experience with AWS Bedrock Agents and Guardrails.
Familiarity with model distillation and prompt optimization techniques.
Understanding of responsible AI practices and AI security.
AWS Certified Machine Learning – Specialty certification.
AWS Certified Solutions Architect certification.
General AI/ML certifications, such as TensorFlow Developer or Azure AI Engineer.

Responsibilities

Design and implement generative AI solutions using Amazon Bedrock, foundation models, and RAG architectures.
Build repeatable intelligent solutions and bots for document processing and data cleansing.
Develop and deploy scalable ML/AI models on AWS infrastructure.
Create API endpoints and integrations for AI/ML services.
Implement model evaluation, monitoring, and continuous improvement processes.
Collaborate with cross-functional teams to embed AI capabilities across business functions.
Design, maintain, and optimize intelligent automation solutions.
Troubleshoot AI Center and resolve issues that arise after implementation.
Build generative AI applications supporting continuous improvement, learning, and augmented decision-making.