Senior Machine Learning Engineer Pennant Solutions Group Richmond, VA Job Details Full-time 16 hours ago Qualifications AI models Data preprocessing Continuous Delivery (CD) implementation Testing and evaluation Amazon SageMaker Classification (ML) Research AI platforms (beyond public GPTs) Scalable systems Infrastructure architecture design Classification analysis Production systems Machine learning cloud services Sentiment analytics Model deployment NER Developing large-scale AI models Engineering research Mentoring Improving predictive model accuracy Natural language processing Developing data pipelines Text classification Scalability Model training Algorithm design research A/B testing DevOps automation Senior level Model evaluation

Full Job Description Senior Machine Learning Engineer Position Type:

Full-Time/Onsite in Richmond, VA -

NO REMOTE Level:

Senior Department:

Engineering About the Role We are seeking an exceptional Senior Machine Learning Engineer to join our growing AI team. This role offers a unique opportunity to architect, develop, and deploy cutting-edge machine learning solutions that directly impact millions of users. As a senior member of our team, you will lead the design and implementation of scalable ML systems, with a particular focus on Natural Language Processing applications and robust MLOps practices. The ideal candidate will bring deep expertise in production machine learning systems, demonstrating proficiency in MLOps, Natural Language Processing, and AWS SageMaker. You will work at the intersection of research and engineering, translating complex ML concepts into production-ready solutions while mentoring junior team members and establishing best practices across the organization. Key Responsibilities Machine Learning Development & Deployment Design, develop, and deploy sophisticated machine learning models with emphasis on NLP applications including text classification, named entity recognition, sentiment analysis, language generation, and semantic search Build and optimize end-to-end ML pipelines using AWS SageMaker, from data preprocessing and feature engineering through model training, evaluation, and deployment Implement state-of-the-art transformer-based models and fine-tune large language models for domain-specific applications Conduct rigorous experimentation, A/B testing, and model evaluation to ensure optimal performance and business impact Develop custom algorithms and innovative solutions for complex business problems that cannot be solved with off-the-shelf approaches MLOps & Infrastructure Architect and maintain robust MLOps infrastructure ensuring seamless model lifecycle management from development to production Implement comprehensive CI/CD pipelines for ML models using tools such as SageMaker Pipelines, MLflow, or similar platforms