AI/ML Engineer

AI/ML Engineer

Req number:

R7330

Employment type:

Full time

Worksite flexibility:

Remote

Who we are

CAI is a global services firm with over 9,000 associates worldwide and a yearly revenue of $1.3 billion+. We have over 40 years of excellence in uniting talent and technology to power the possible for our clients, colleagues, and communities. As a privately held company, we have the freedom and focus to do what is right—whatever it takes. Our tailor-made solutions create lasting results across the public and commercial sectors, and we are trailblazers in bringing neurodiversity to the enterprise.

Job Summary

We are looking for a motivated AI/ML Engineer ready to take us to the next level! you will be responsible for developing and deploying machine learning and deep learning solutions for engineering applications, focusing on product design and manufacturing process development and are looking for your next career move, apply now.

Job Description

We are looking for an AI/ML Engineer to design and implement scalable AI/ML solutions for engineering challenges. This position will be full-time and remote.

What You’ll Do

• Develop and maintain high-performance AI/ML infrastructure (local and cloud-based) to support AI hub projects and engineering users

• Build and deploy scalable machine learning pipelines using TensorFlow, PyTorch, Keras, and other deep learning frameworks for production environments

• Implement classical machine learning algorithms including regression models, classification algorithms, clustering techniques, dimensionality reduction methods, and ensemble methods (Random Forests, XGBoost, LightGBM)

• Design and deploy deep learning architectures including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), autoencoders, and transformer architectures for non-generative tasks

• Apply inverse design principles to optimize engineering solutions using AI-driven approaches, enabling data-driven design optimization

• Develop physics-informed neural networks (PINNs) and hybrid AI models that integrate engineering domain knowledge with machine learning capabilities

• Implement surrogate modeling techniques to accelerate engineering simulations and enable real-time optimization

What You'll Need

Required:

• 4-6 years of relevant experience

• Proven track record of developing and deploying AI/ML solutions for engineering, scientific, or industrial applications

• Demonstrated experience in successfully delivering end-to-end machine learning projects from conception to production deployment

• Deep understanding of classical machine learning algorithms: linear regression, logistic regression, Support Vector Machines (SVM), decision trees, random forests, gradient boosting machines (XGBoost, LightGBM, CatBoost), k-means clustering, hierarchical clustering, Principal Component Analysis (PCA), and other dimensionality reduction techniques

• Strong expertise in deep learning architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), autoencoders, variational autoencoders, attention mechanisms, residual networks (ResNets), and transformer architectures for non-generative applications

• Hands-on experience with TensorFlow, PyTorch, Keras, scikit-learn, XGBoost, and related ML/DL libraries and frameworks

• Knowledge of inverse design principles, optimization algorithms (gradient descent variants, genetic algorithms, particle swarm optimization), and AI-driven engineering design methodologies

• Experience with physics-informed machine learning, multi-objective optimization, and constraint-based optimization

• Familiarity with computer vision techniques, time-series analysis, anomaly detection, and predictive maintenance applications

• Understanding of feature engineering, feature selection, data augmentation techniques, and handling imbalanced datasets

• Experience with model interpretability and explainable AI techniques (SHAP, LIME, attention visualization, feature importance analysis)

• Knowledge of transfer learning, domain adaptation, and few-shot learning techniques

• Understanding of neural network optimization, loss function design, and training strategies for complex engineering problems

Physical Demands

• Ability to safely and successfully perform the essential job functions

• Sedentary work that involves sitting or remaining stationary most of the time with occasional need to move around the office to attend meetings, etc.

• Ability to conduct repetitive tasks on a computer, utilizing a mouse, keyboard, and monitor

Reasonable accommodation statement

If you require a reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employment selection process, please direct your inquiries to application.accommodations@cai.io or (888) 824 – 8111.