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# Senior AI/ML Engineer

- **Company:** Spectraforce
- **Location:** Atlanta, GA, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Artificial Neural Networks, Association Rule Learning, Big Data, Cluster Analysis, Computer Programming, Data Reduction, Linear Regression, Logistic Regression, Machine Learning, Support Vector Machine, Unstructured Data, Reinforcement Learning, Supervised Learning, Feature Engineering, Data Ingestion, Random Forest, Gaussian, Information Technology, Xgboost, Variational Autoencoders, Machine Learning Operations, K Means, Unsupervised Learning
- **Published:** August 13, 2026
- **Apply:** http://leoforce.us/Careers/Spectraforce/JobDetails.html?jobid=7ddd89ef-60b2-4008-8dc6-3c29c2bd5f7c&OrgId=1&UserId=6557

## About the Role

1. Strong programming experience in Python, * Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related quantitative field (Ph.D. is a plus).
 * 5-9 years of hands-on experience in machine learning and data science roles.
 * Strong mathematical foundation - linear algebra, calculus, probability, and statistics.
 * Demonstrated ability ttake ML projects from research tproduction.
 * Experience working with structured and unstructured data at scale.

Required Technical Expertise

 * Supervised Learning
 * Linear regression and logistic regression,
 * Decision trees, Random Forest, Gradient Boosting (XGBoost, LightGBM, CatBoost),
 * Support Vector Machines (SVMs) and kernel methods,
 * Neural networks - CNNs, RNNs, LSTMs, and Transformers,
 * Classification, regression, and ranking problems,
 * Cross-validation, bias-variance trade-off, regularization (L1/L2, dropout)
 * Unsupervised Learning
 * Clustering: K-Means, DBSCAN, Gaussian Mixture Models, hierarchical clustering
 * Dimensionality reduction: PCA, t-SNE, UMAP
 * Autoencoders and variational autoencoders (VAEs)
 * Anomaly detection and outlier identification
 * Association rule mining (Apriori, FP-Growth)

## Description

* Design and implement supervised, unsupervised, and reinforcement learning models tailored tcomplex business problems.
 * Conduct exploratory data analysis, feature engineering, and statistical modelling on large-scale datasets.
 * Evaluate model performance using appropriate metrics and validation techniques; iterate timprove accuracy and robustness.
 * Build and maintain end-to-end ML pipelines from data ingestion tmodel serving and monitoring in production.
 * Collaborate with data engineers, software engineers, and business stakeholders ttranslate requirements intML solutions.
 * Research, prototype, and integrate state-of-the-art algorithms and frameworks tsolve novel problems.
 * Document models, experiments, and design decisions tensure reproducibility and knowledge sharing.
 * Stay current with advances in ML research and assess applicability tthe organization's use cases.


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