AI/ML Engineer - Computer Vision at Prime Solutions Group Inc

Goodyear, Arizona, United States -

Full Time

Start Date

Immediate

Expiry Date

02 Sep, 26

Salary

180000.0

Posted On

04 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Computer Vision, Machine Learning, Deep Learning, Python, PyTorch, TensorFlow, CNNs, Vision Transformers, Object Detection, Image Segmentation, MLOps, Model Optimization, Hyperparameter Tuning, Image Analysis, Git, Data Science

Industry

IT Services and IT Consulting

Description

Description Prime Solutions Group (PSG), Inc. is an innovative digital engineering company founded in 2007 and headquartered in Goodyear, AZ. We specialize in advanced sensing, AI/ML, and digital engineering solutions, partnering with many of the nation’s leading defense companies to deliver mission-critical technology. Our work spans the full system lifecycle—from R&D to operational deployment—supporting the Department of Defense, Intelligence Community, and federal partners. At PSG, you’ll join a small, agile team where your contributions have a direct impact while working alongside top-tier engineering talent Position Description: Develop AI-Powered Computer Vision Solutions for Real-World Mission Applications Prime Solutions Group (PSG), Inc. is seeking an experienced AI/ML Computer Vision Engineer to design, develop, and deploy advanced computer vision and machine learning solutions supporting mission-critical national security programs. In this role, you will work at the forefront of artificial intelligence, applying state-of-the-art deep learning techniques to challenging real-world problems involving image, video, and sensor data. You will be responsible for developing and optimizing machine learning models, conducting experimentation, improving model performance, and transitioning AI capabilities into operational environments. This position is ideal for engineers who enjoy solving complex technical challenges, working with large-scale datasets, and turning cutting-edge research into production-ready solutions. Responsibilities include: Design, develop, train, and deploy machine learning and computer vision models for real-world mission applications. Build and optimize deep learning architectures, including CNNs, Vision Transformers (ViTs), and hybrid AI/ML models. Perform hyperparameter tuning, model optimization, and performance evaluation to improve accuracy, robustness, and efficiency. Develop and maintain training, validation, testing, and inference pipelines for AI/ML systems. Analyze model performance, identify failure modes, and implement improvements to increase reliability and generalization. Work with large image, video, and sensor datasets to support object detection, classification, segmentation, tracking, and anomaly detection tasks. Collaborate with software engineers and MLOps teams to integrate models into production environments. Evaluate emerging AI technologies and recommend new approaches that improve mission outcomes. Create technical documentation, experiment reports, and performance assessments. Support technical reviews, customer briefings, and cross-functional engineering efforts. Requirements U.S. Citizenship Active Top-Secret Clearance with eligibility to obtain an SCI with CI Poly Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Electrical Engineering, Applied Mathematics, or a related technical field. 4+ years of experience developing AI/ML solutions in production, research, or applied engineering environments. Proven experience delivering models using Python and ML frameworks (TensorFlow, PyTorch, Keras, etc.). Experience training and evaluating machine learning models using large datasets. Experience developing and training computer vision models using deep learning techniques. Familiarity with CNNs, Vision Transformers, object detection, classification, and segmentation. Experience with model tuning, hyperparameter optimization, and performance evaluation. Strong understanding of computer vision fundamentals and image analysis techniques. Ability to improve model accuracy, robustness, and operational performance. Familiarity with software engineering best practices, Git, and collaborative development workflows. Preferred Skills or Experience: Experience with generative AI technologies, including diffusion models and image generation systems. Familiarity with Vision-Language Models (VLMs), multimodal AI, Large Vision Models (LVMs), and Retrieval-Augmented Generation (RAG). Experience with object tracking, multi-object tracking, or video analytics. Experience applying computer vision techniques to imagery, remote sensing, or sensor-based data. Familiarity with reinforcement learning or autonomous systems. Experience with MLOps tools and practices, including Docker, Kubernetes, MLflow, Airflow, and CI/CD pipelines. Experience deploying AI/ML solutions in AWS, Azure, or Google Cloud environments. Experience supporting defense, intelligence, aerospace, or other mission-critical programs. Why Join PSG? At PSG, you'll work on challenging AI problems that directly support national security missions. You'll have the opportunity to collaborate with highly skilled engineers and researchers while helping shape the next generation of intelligent sensing, computer vision, and AI-enabled systems. We offer: Competitive compensation and benefits Professional development and tuition assistance Flexible and collaborative engineering culture Exposure to cutting-edge AI/ML technologies Direct impact on mission-critical programs Opportunities to grow into technical leadership roles Bring your passion for AI, computer vision, and machine learning to PSG and help build the next generation of intelligent mission systems. Salary Description Salary range starts at $110,000 with the potential for higher compensation based on experience, skills, and mission needs

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Responsibilities

Design, develop, and deploy AI-powered computer vision models for national security mission applications. Build and optimize deep learning architectures while maintaining training and inference pipelines for production environments.