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, 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)

Why Should You Apply?

Health Benefits

Referral Program

Excellent growth and advancement opportunities

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