Senior AI/ML Engineer

Senior AI/ML Engineer Our client, an IT Services and Consulting company, is looking for a Senior AI/ML Engineer for their Atlanta, GA/Hybrid location. Responsibilities: Design and implement supervised, unsupervised, and reinforcement learning models tailored to complex 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 to improve accuracy and robustness. Build and maintain end-to-end ML pipelines from data ingestion to model serving and monitoring in production. Collaborate with data engineers, software engineers, and business stakeholders to translate requirements into ML solutions. Research, prototype, and integrate state-of-the-art algorithms and frameworks to solve novel problems. Document models, experiments, and design decisions to ensure reproducibility and knowledge sharing. Stay current with advances in ML research and assess applicability to the organization's use cases. Requirements: Strong programming experience in Python Algorithms knowledge and knowledge on utilizing right python package Strong ML and DS skills 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 to take ML projects from research to production. Experience working with structured and unstructured data at scale. 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) 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) Topic modelling (LDA, NMF) Markov Decision Processes (MDPs) states, actions, rewards, transitions Model-free methods: Q-Learning, SARSA, Deep Q-Networks (DQN) Policy gradient methods: REINFORCE, PPO, A3C / A2C Actor-Critic architectures Multi-armed bandits and contextual bandits Reward shaping, environment design, and simulation frameworks (OpenAI Gym) Relevant learning algorithms - Adjacent & advanced techniques Transfer learning and fine-tuning pre-trained models Semi-supervised and self-supervised learning Active learning and human-in-the-loop pipelines Federated learning for privacy-preserving training Bayesian optimization and hyperparameter tuning (Optuna, Ray Tune) Ensemble methods, stacking, and model blending Graph Neural Networks (GNNs) a plus Causal inference and counterfactual reasoning — a plus Experience with Large Language Models (LLMs), prompt engineering, or fine-tuning foundation models. Exposure to real-time ML systems and low-latency inference pipelines. Publications, open-source contributions, or participation in ML competitions (Kaggle, etc.). Domain expertise in fintech, healthcare, e-commerce, or a related industry. Years of Experience: 10.00 Years of Experience Why Should You Apply? Health Benefits Referral Program Excellent growth and advancement opportunities