Machine Learning Engineer

Career Category Engineering Job Description Role Description We are seeking an experienced Machine Learning Engineer to design, develop, deploy, and support scalable machine learning solutions. This role will develop predictive models, build reusable machine learning pipelines, operationalize models through MLOps practices, and monitor model performance in production. The ideal candidate has strong hands-on experience with machine learning algorithms, predictive modeling, forecasting, Python, feature engineering, model training and evaluation, AWS cloud services, and production ML operations. Experience developing Generative AI and Large Language Model applications is also preferred. The candidate will collaborate with data scientists, data engineers, software engineers, product teams, and business stakeholders to deliver secure, reliable, and scalable machine learning solutions. Roles and Responsibilities Design, develop, train, evaluate, and deploy predictive machine learning models for time-series forecasting, classification, regression, anomaly detection, clustering, recommendation, and other business use cases. Perform data exploration, preprocessing, feature engineering, feature selection, and model experimentation. Select appropriate machine learning algorithms, forecasting methods, and evaluation metrics based on business and technical requirements. Build reusable machine learning pipelines covering data ingestion, feature engineering, training, validation, deployment, monitoring, and retraining. Develop forecasting solutions using historical data, time-series features, backtesting , and appropriate validation techniques. Optimize model performance through hyperparameter tuning, cross-validation, experimentation, and error analysis. Develop and maintain production APIs and services that expose machine learning capabilities to applications and downstream consumers. Implement MLOps practices, including experiment tracking, model versioning, model registries, automated testing, CI/CD, and reproducible deployments. Develop, deploy, and operate machine learning workloads primarily on AWS. Develop monitoring and alerting solutions for model accuracy, forecast performance, data quality, drift, bias, latency, reliability, and infrastructure performance. Establish automated or controlled model-retraining and deployment processes. Conduct A/B testing and experimentation to evaluate model and application effectiveness. Develop machine learning solutions that are scalable, secure, explainable, maintainable, and cost-efficient. Implement responsible AI, security, privacy, access-control, and governance requirements. Troubleshoot model, data, pipeline, application, and production-environment issues. Develop Generative AI applications using Large Language Models and Retrieval-Augmented Generation where appropriate . Build LLM solutions involving document processing, chunking, embeddings, vector search, prompt engineering, evaluation, and monitoring. Collaborate with data scientists, data engineers, software engineers, DevOps teams, product teams, and business stakeholders. Participate in technical design discussions, code reviews, sprint planning, backlog refinement, and estimation activities. Maintain model documentation, technical specifications, operational procedures, and deployment standards. Stay current with advances in machine learning, forecasting, MLOps , Generative AI, and cloud technologies. Participate in production support activities, including occasional off- hours support. Functional Skills Must-Have Skills Strong foundation in supervised and unsupervised machine learning algorithms, predictive modeling, statistical methods, and model evaluation. Strong hands-on experience with Python and SQL. Experience with machine learning libraries such as Scikit-learn, PyTorch , TensorFlow, XGBoost , or equivalent technologies. Experience with data preprocessing, feature engineering, model selection, model training, hyperparameter tuning, and evaluation. Hands-on experience developing predictive models and time-series forecasting solutions, including feature engineering, backtesting , model evaluation, and performance monitoring. Experience developing and deploying production machine learning models. Understanding of classification, regression, forecasting, clustering, anomaly detection, and recommendation techniques. Experience implementing MLOps pipelines for model development, deployment, monitoring, versioning, and retraining. Experience with experiment tracking, model registries, data versioning, and reproducible machine learning workflows. Hands-on experience with cloud-