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.

• 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, 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)
• 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 / A2C
• Actor-Critic architectures
• Multi-armed bandits and contextual bandits
• Reward shaping, environment design, and simulation frameworks (OpenAI Gym)

• Health Benefits
• Referral Program
• Excellent growth and advancement opportunities