AI/ML Engineer — Production ML & Data Pipelines (TS/SCI)

Medium

Maryland

On-site

USD 150,000 - 270,000

Full time

14 days+

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Benefits offered by this job

Comprehensive health benefits
401(k) retirement plan
Flexible work options
Professional development support

Job summary

Synergy ECP is seeking an AI/ML Engineer in Maryland to build, deploy, and maintain machine learning models at scale. The role involves developing and training models, deploying them into production, and collaborating with software and DevOps teams. Ideal candidates will have a B.S. degree in a technical field along with 3+ years of experience. The compensation ranges from $150,000 to $270,000, alongside a comprehensive benefits package that includes health benefits, a 401(k), paid time off, and more.

Qualifications

  • 3+ years of experience in machine learning or related fields.
  • Strong knowledge of model training, deployment, and performance optimization.

Responsibilities

  • Develop and train machine learning models.
  • Deploy models into production environments.
  • Build data pipelines and workflows for model training and inference.
  • Monitor model performance and optimize over time.

Skills

Python and ML frameworks (TensorFlow, PyTorch, Scikit‑learn)
Experience with data processing and feature engineering
Understanding of model evaluation and performance tuning
Experience deploying ML in production environments

Education

B.S. degree in a technical field

Tools

Cloud ML services (AWS SageMaker, Azure ML)

Job description

Synergy ECP is seeking an AI/ML Engineer in Maryland to build, deploy, and maintain machine learning models at scale. The role involves developing and training models, deploying them into production, and collaborating with software and DevOps teams. Ideal candidates will have a B.S. degree in a technical field along with 3+ years of experience. The compensation ranges from $150,000 to $270,000, alongside a comprehensive benefits package that includes health benefits, a 401(k), paid time off, and more.
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