LETITBEX AI
Website:
letitbexai.com
Job details:
About LetitbexAI
LetitbexAI is a fast-growing AI-driven technology company focused on building intelligent, scalable, and enterprise-grade solutions. We work at the intersection of AI, data engineering, cloud, and business transformation, helping organizations unlock real value from artificial intelligence.
Position: AI/ML Engineer
Experience: 5 - 10 Years
Notice Period: Can be considered up 15 Days
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
Required
What You'll Need
- 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
Click on Apply to know more.