Senior Machine Learning Engineer

Pennant Solutions Group; Richmond, Northern (VA, KY); On-site; USD 140,000 - 200,000; Full time

Job summary

Pennant Solutions Group is seeking a Senior Machine Learning Engineer in Richmond, VA to design, develop, and deploy advanced NLP-focused ML systems. You will lead production-grade ML pipelines and MLOps practices, mentoring teammates and shaping best practices across the organization.

The role emphasizes hands-on development with AWS SageMaker, transformer models, and scalable ML architectures, with on-site collaboration in our Richmond office.

Qualifications

• Senior ML engineer with production ML experience
• Strong NLP knowledge with practical deployments
• Experience with AWS SageMaker and ML pipelines
• Mentor junior engineers and establish best practices

Responsibilities

• Design, develop, and deploy ML models with emphasis on NLP applications
• Build end-to-end ML pipelines using AWS SageMaker
• Implement transformer-based models and fine-tune large language models
• Conduct experiments, A/B testing, and model evaluation
• Architect and maintain robust MLOps infrastructure and CI/CD for ML

Skills

NLP

MLOps

AWS SageMaker

Transformer models

Production ML

Tools

SageMaker Pipelines

MLflow

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