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AI​/ML Engineer II

Job in Fort Meade, Anne Arundel County, Maryland, USA
Listing for: Benz Technology
Full Time position
Listed on 2026-09-12
Job specializations:
  • Software Development
    Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Data Scientist
Salary/Wage Range or Industry Benchmark: 110000 - 170000 USD Yearly USD 110000.00 170000.00 YEAR
Job Description & How to Apply Below

Active TS/SCI security clearance required. Benz Technology is unable to sponsor clearances. U.S. citizenship required.

About the Role

As an AI/ML Engineer II, you will design, develop, and deploy machine learning models and AI-driven solutions to support mission-critical analytical and operational needs. You will work within a modeling, testing, and evaluation team to build and refine intelligent systems that drive insight and automation. This role sits at the intersection of data science, software engineering, and applied AI research.

Responsibilities
  • Design and implement machine learning models and pipelines for operational use cases
  • Evaluate model performance and iterate on approaches to improve accuracy and reliability
  • Collaborate with data engineers and analysts to prepare and validate training datasets
  • Integrate ML models into production systems and workflows
  • Document methodologies, experiments, and results for technical and non-technical stakeholders
  • Research and apply emerging AI/ML techniques relevant to team mission objectives
Required Qualifications
  • Hands-on experience developing and deploying machine learning or deep learning models
  • Proficiency in Python and ML frameworks such as Tensor Flow, PyTorch, or scikit-learn
  • Experience with data preprocessing, feature engineering, and model evaluation
  • Familiarity with MLOps practices and model lifecycle management
Desired Qualifications
  • Experience with NLP, computer vision, or anomaly detection applications
  • Familiarity with cloud-based ML platforms (AWS Sage Maker, Azure ML, or GCP Vertex AI)
  • Experience working in modeling, testing, and evaluation (MT&E) environments
  • Knowledge of containerization tools such as Docker or Kubernetes for model deployment
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