Senior AI/ML Engineer

• 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.

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)

1. Algorithms knowledge and knowledge on utilizing right python package
2. Strong ML and DS skills, * 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