Senior AI Engineer

SAIC

. Design, develop, and deploy predictive models using regression, classification, clustering, and neural networks .

Posted 9/23/2026full-timeRemote • South Dakota • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying AI/ML solutions, with a strong focus on Document AI applications. Proficient in Python, modern ML frameworks, and AWS services, while ensuring compliance with cybersecurity policies and maintaining high-quality code.

Highest-signal resume keywords

Python ProgrammingMachine Learning ExpertiseAWS Services ProficiencyTerraform ConfigurationDocument AI Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data AnalysisRegressionClassificationClusteringNeural NetworksCI/CD PipelinesImage ProcessingSelf-Supervised LearningTransformer ModelsOpenCV

Soft Skills

CollaborationCommunicationProblem-Solving

Tools & Technologies

TensorFlowPyTorchGitLabGitLab RunnerAWS BedrockAWS LambdaAWS ECSAWS S3AWS Aurora RDSAWS ELB

Certifications & Qualifications

AWS Certified Solutions ArchitectAWS Certified DeveloperAWS Certified Machine LearningAWS Certified SysOps AdministratorAWS Certified Cloud Practitioner

Industry Keywords

AI SolutionsML SolutionsNLP SolutionsCybersecurity ComplianceDocument Processing

Tech Stack

Tools & technologies

AWSCloudCyber SecurityHibernateJavaPythonPyTorchSpringSpring BootSpringBootSQLTensorflowTerraform

About the role

Key responsibilities & impact

Design, develop, and deploy predictive models using regression, classification, clustering, and neural networks
Architect and implement end-to-end AI/ML/NLP solutions compliant with cybersecurity and enterprise policies
Produce maintainable, efficient, reliable, secure, fault-tolerant, and well-documented code
Identify and resolve performance bottlenecks, security vulnerabilities, and technical challenges across the AI/ML stack
Build and maintain CI/CD pipelines using Terraform, GitLab, and GitLab Runner, including automated testing, quality checks, and security scanning
Support production operations through deployments, smoke testing, monitoring, incident/root cause analysis, and issue resolution
Participate in Agile processes, including user-story refinement, task estimation, sprint backlog creation, reviews, demos, and retrospectives
Collaborate with product, architecture, DevOps, security, and QA teams
Design and run experiments, analyze results, and fine-tune models for Document AI use cases
Document technical designs, models, and processes and communicate findings to technical and non-technical stakeholders

Requirements

What you’ll need

Bachelor’s degree or higher in Computer Science, Machine Learning, or a related field; 4 years of experience may be substituted for the degree
18 years of experience, including 13+ years of overall professional Software/IT experience
7+ years of hands-on experience in Data Analysis and Machine Learning
Experience maintaining and enhancing machine learning systems, preferably for document processing and Document AI
Strong proficiency in Python and modern ML libraries/frameworks, including TensorFlow and PyTorch
Expertise with AWS services including Bedrock, Lambda, ECS, SQS, SNS, S3, ELB, ALB, and Aurora RDS
Experience creating Terraform configurations and using GitLab Runner to deploy software in cloud environments
Expertise with image transformer models for document image understanding, such as Microsoft’s DiT
Experience implementing self-supervised learning techniques for large-scale unlabeled text images
Experience applying Transformer models to Document AI tasks, including document image classification and document layout analysis
Ability to use self-supervised pre-trained models as backbone networks for downstream Document AI tasks
Proficiency designing experiments, analyzing outcomes, tuning models, and communicating results
Familiarity integrating Transformer models into OCR pipelines
Understanding of image processing techniques and OpenCV for document image analysis
At least one current AWS certification required, such as AWS Certified Solutions Architect, Developer, Machine Learning, SysOps Administrator, or Cloud Practitioner
Desired: Java, SQL, relational databases, web services, REST APIs, Spring, Spring Boot, Hibernate, JPA, MyBatis, JBoss/Fuse, Camel, AMQ, and broader AWS architecture and integration patterns