Senior AI/ML Engineer

Our Client, an IT Services and Consultant company, is looking for a Senior AI/ML Engineer for their Atlanta, GA/Remote 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: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. Required Technical Expertise Supervised Learning Linear regression and logistic regression, Decision trees, Random Forest, Gradient Boosting (XGBoost, LightGBM, Cat Boost), 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, UMAPAutoencoders and variational autoencoders (VAEs)Anomaly detection and outlier identification Association rule mining (Apriori, FP-Growth)Topic modelling (LDA, NMF)Reinforcement Learning Markov Decision Processes (MDPs) states, actions, rewards, transitions Model-free methods: Q-Learning, SARSA, Deep Q-Networks (DQN)Policy gradient methods: REINFORCE, PPO, A3C / A2CActor-Critic architectures Multi-armed bandits and contextual bandits Reward shaping, environment design, and simulation frameworks (OpenAI Gym)Why Should You Apply? Health Benefits Referral Program Excellent growth and advancement opportunities