Senior AI/ML Engineer — Build Production-Grade AI Solutions

Parsons; Aberdeen (MD); On-site; USD 150,000 - 210,000; Full time

Job summary

Parsons is seeking an AI/ML Engineer to design, develop, deploy, and maintain AI/ML solutions that support business, operational, and customer-facing products. You will collaborate with data scientists, software engineers, product owners, and stakeholders to define requirements and implement scalable models.

The role emphasizes production-grade ML, data pipelines, model monitoring, and adherence to security, privacy, and governance standards, with an opportunity to work across federal solutions

Qualifications

• 8+ years of related professional experience in software engineering, machine learning engineering, data science, or AI development.
• Experience deploying ML models in production environments.
• Advanced knowledge of ML concepts, statistical modeling, and predictive analytics.
• Ability to communicate complex technical concepts to non-technical audiences and stakeholders.

Responsibilities

• Designs, builds, trains, evaluates, and deploys AI/ML models to solve business problems.
• Develops and maintains data pipelines and feature engineering processes.
• Monitors model performance, drift, and reliability in production; implements corrective actions.
• Collaborates with data scientists, engineers, product owners, and business stakeholders on requirements.
• Supports MLOps practices including model versioning, deployment automation, and monitoring.

Skills

Python proficiency

ML model deployment

Data pipelines

Cloud platforms

Education

Bachelor's degree in CS/DS/AI/ML/Engineering/Statistics

Tools

TensorFlow

PyTorch

Scikit-learn

Keras

Hugging Face

AWS SageMaker

Job description

Parsons is seeking an AI/ML Engineer to design, develop, deploy, and maintain AI/ML solutions that support business, operational, and customer-facing products. You will collaborate with data scientists, software engineers, product owners, and stakeholders to define requirements and implement scalable models.

The role emphasizes production-grade ML, data pipelines, model monitoring, and adherence to security, privacy, and governance standards, with an opportunity to work across federal solutions