Senior Artificial Intelligence Engineer

Role & responsibilities

• Expertise in supervised, unsupervised, machine learning, deep learning, reinforcement learning, statistics techniques.
• Proficiency in Python, PyTorch, TensorFlow.
• Good to have knowledge of Bayesian inference, probability distribution, hypothesis testing, A/B testing, and time series forecasting.
• Hands-on experience with feature engineering and hyperparameter tuning.
• Experience with MLflow, Weights & Biases, DVC (Data Version Control).
• Ability to track model performance across multiple experiments and datasets.
• End-to-end ML lifecycle management from Data ingestion, preprocessing, feature engineering, model training, deployment, and monitoring.
• Expertise in CI/CD for ML, containerization (Docker, Kubernetes), and orchestration (Kubeflow).
• Good to have experience in automating data labelling and feature stores (Feast).
• Good to have data processing experience in Spark, Flink, Druid, Nifi.
• Good to have real-time data streaming in Kafka and other messaging systems
• Designing and optimizing data pipelines for structured data.
• Good to have hands-on experience with Pyspark.
• Strong SQL skills for data extraction, transformation, and query optimization.
• Atleast one database experience such as NOSQL database.
• Implementing parallel and distributed ML techniques for large-scale systems.
• Good to have knowledge of model explainability (SHAP, LIME), bias mitigation, and adversarial robustness.
• Experience in model drift monitoring.
• Good to have CUDA for GPU acceleration.
• Good to have choosing between CPU/GPU for different ML workloads (batch inference on CPU, training on GPU).
• Good to have scaling deep learning models on multi-GPU.
• Strong presentation skills, including data storytelling, visualization (Matplotlib, Seaborn, Superset), and report writing.
• Experience in mentoring data scientists, research associates, and data engineers.
• Contributions to research papers, patents, or open-source projects.