Senior AI Engineer

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.

Key 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.

Required:

- 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.
- Ability
- 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

Desired:

- 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.

Certifications

- 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