Machine Learning Engineer

• 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.