AI/ML Engineer (Midlevel)

Node.Digital•Herndon, VA

20d•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, operational efficiency, and continuous refinement of integrated AI/ML solutions. The engineer will develop generative AI applications and intelligent automation in support of continuous improvement, learning, and augmented decision-making, including troubleshooting and resolving post-implementation issues. The position is based in Herndon, Virginia, with hybrid work. U.S. citizenship is preferred. The role includes designing generative AI solutions using Amazon Bedrock, foundation models, and retrieval-augmented generation (RAG); building reusable bots for document processing and data cleansing; deploying scalable AI/ML models on AWS; and developing API endpoints and integrations. The engineer will also implement model evaluation, monitoring, and improvement processes and collaborate with cross-functional teams to embed AI capabilities across business functions. The position requires a bachelor's degree and at least five years of experience in automation engineering, AI/ML integrations, and generative AI application development. Node.Digital describes itself as an innovative solutions development company serving government and commercial clients with cloud, mobile, and AI/machine-learning technologies.

Requirements

Bachelor's degree.
At least five years of experience in automation engineering roles focused on AI/ML integrations and generative AI application development.
At least five 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.
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 and database concepts; experience with SQL Server or PostgreSQL.
Knowledge of AWS cloud architecture patterns and serverless computing.
Experience with CI/CD pipelines and DevSecOps practices.
Experience with toolchains including TFS, SVN, and Git.
Experience participating in different SDLC phases and exposure to SDLC approaches such as Agile.
Effective communication skills for technical discussions.
Ability to work remotely and adapt to varying organizational structures.
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.
Experience with front-end frameworks such as React, Angular, or Vue.js.
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.
Data analysis and data architecture skills.
Alignment with the customer's mission and values.

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.
Maintain and optimize intelligent automation solutions, including troubleshooting and resolving issues after implementation.
Build generative AI applications that support continuous improvement, learning, and augmented decision-making.