Machine Learning Engineer

About The Role

We are looking for a skilled and motivated Machine Learning Engineer / Data Scientistto join our team. The ideal candidate should have a strong foundation in machinelearning and deep learning, with experience in building end-to-end ML pipelines andhybrid modeling approaches that incorporate both physics-based simulations anddata-driven insights. You will work on high-impact projects involving time seriesforecasting, predictive modeling, and system simulation to support data-drivendecision-making.Key Responsibilities? Design, train, and evaluate machine learning and deep learning models forregression, classification, and forecasting tasks.? Build and maintain end-to-end ML pipelines, including data ingestion,preprocessing, feature engineering, model training, validation, and deployment.? Develop physics-informed or hybrid models by combining domain knowledgewith machine learning approaches to simulate and predict system behaviors.? Implement and optimize time series forecasting models using statisticaltechniques, classical ML, and deep learning architectures (e.g., LSTM,Transformer).? Collaborate with cross-functional teams to transtranslate business needs intomachine learning solutions.? Monitor and refine model performance post-deployment to ensure robust,scalable outcomes.

Key Skills Required

? Strong knowledge of Machine Learning and Deep Learning algorithms and theirapplications.? Proven experience in building and deploying end-to-end ML pipelines.? Hands-on experience in physics-based modeling or hybrid approachescombining physical models with data-driven techniques.Solid understanding of time series forecasting using statistical (ARIMA, ETS),machine learning (XGBoost, RF), and deep learning (RNN, LSTM, Temporal CNN)methods.? Experience designing regression and classification models for real-worldproblems.? Proficiency in Python, and libraries like TensorFlow / PyTorch, scikit-learn,pandas, NumPy, statsmodels, etc.? Strong problem-solving mindset and ability to work with minimal supervision.Skills: deep learning,model training,statistical techniques,pytorch,scikit-learn,physics-based modeling,end-to-end ml pipelines,machine learning,feature engineering,time series forecasting,tensorflow,python,numpy,hybrid modeling,classification,pandas,deployment,preprocessing,time series analysis,statsmodels,regression,validation,data ingestion