Senior AI Engineer

SAIC•Pierre, SD

19h

About The Position

SAIC is looking for a Senior AI Engineer who will serve as a key technical leader within a high-performing development team, responsible for designing, implementing, and operationalizing advanced AI/ML/NLP solutions in AWS cloud-native environments. The ideal candidate has deep expertise in machine learning, data analytics, and modern software engineering practices, with proven experience building and maintaining document-centric AI systems at scale.

Requirements

Bachelor’s degree or higher in Computer Science, Machine Learning, or a related field. (4 years experience in lieu of degree) and 18 years experience.
13+ years of overall professional experience in Software/IT
7+ years of hands-on experience in Data Analysis and Machine Learning.
Proven experience maintaining and enhancing machine learning systems, preferably focused on document processing and Document AI.
Strong proficiency in Python and modern ML libraries/frameworks such as TensorFlow and PyTorch.
Demonstrated expertise with AWS services, including (but not limited to): Bedrock, Lambda, ECS, SQS, SNS.
Hands-on experience creating Terraform configurations and using GitLab Runner to deploy working software in cloud environments.
Proven expertise working with image transformer models for document image understanding, such as Microsoft’s DiT.
Demonstrated experience implementing self-supervised learning techniques, particularly for pre-training models on large-scale unlabeled text images (e.g., approaches similar to Microsoft’s DiT).
Practical experience applying Transformer models to Document AI tasks, including: Document image classification, Document layout analysis
Proven ability to leverage self-supervised, pre-trained models (e.g., DiT) as backbone networks to achieve state-of-the-art results on downstream Document AI tasks.
Proficiency in designing experiments, analyzing outcomes, and tuning models for optimal performance; ability to interpret and communicate experimental results effectively.
Familiarity with integrating Transformer models into OCR pipelines and collaborating with OCR technologies to improve text detection and extraction.
Solid understanding of image processing techniques, including OpenCV usage for resizing, feature extraction, and other preprocessing tasks for document image analysis.
Experience building solutions with AWS services such as ECS, Lambda, S3, SQS, SNS, ELB, ALB, and Aurora RDS
At least one current AWS certification is required, such as: AWS Certified Solutions Architect – Associate, AWS Certified Developer – Associate, AWS Certified Machine Learning – Specialty/Engineer Associate, AWS Certified SysOps Administrator – Associate, AWS Certified Cloud Practitioner

Nice To Haves

Programming experience with Java.
Experience with SQL and relational databases (e.g., Oracle).
Experience with web services and REST-based APIs.
Familiarity with Spring, Spring Boot, Hibernate, JPA, MyBatis ORM frameworks.
Experience with JBoss/Fuse, Camel, and AMQ.
Additional experience in broader AWS architecture and integration patterns.

Responsibilities

Design, develop, and deploy predictive models using machine learning algorithms (regression, classification, clustering, neural networks).
Architect and implement end-to-end AI/ML/NLP solutions that comply with cybersecurity and enterprise policy requirements.
Apply sound software engineering principles to produce code that is maintainable, efficient, reliable, secure, fault-tolerant, and well-documented.
Identify and resolve performance bottlenecks, security vulnerabilities, and other 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, including deployments, smoke testing, monitoring, incident/root cause analysis, and issue resolution.
Participate in Agile development processes: review and refine user stories, estimate tasks, create sprint backlogs, and contribute to sprint reviews, demos, and retrospectives.
Collaborate with cross-functional teams (product, architecture, DevOps, security, QA) to ensure solutions align with client objectives and organizational standards.
Design and run experiments, analyze results, and fine-tune models to optimize performance for Document AI use cases.
Document technical designs, models, and processes; clearly communicate findings and recommendations to technical and non-technical stakeholders.