Poly)

Parsons Corporation•Columbia, MD

1d•Onsite

About The Position

Parsons is looking for an amazingly talented Lead AI/ML Engineer to join our team! As a AI/ML Tech Stack Engineer at Parsons, you'll support the end-to-end lifecycle of AI model development, deployment, and operations. Focusing on building robust AI pipelines, integrating models into production systems, and automating workflows across hybrid computer environments.

Requirements

Active TS/SCI w/ Poly
12+ years of IT Network Engineer of Software Developer/Software Developer in Test experience or equivalent work experience
Bachelor's degree in related field (Computer Science, Cybersecurity, Computer Engineering, etc.); additional years of experience will be considered in lieu of degree
Understanding of regression, classification, clustering, dimensionality reduction
Understanding of regularization, cross-validation, hyperparameter tuning
Primary Languages: Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch) and SQL for data manipulation
Software Engineering: OOP, design patterns, clean code principles
Version Control: Git, GitHub/GitLab workflows
Testing: Unit tests, integration tests for ML pipelines
Understanding of Key Technologies: Flask, FastAPI, Docker, Kubernetes Airflow, Kubeflow, MLflow AWS (SageMaker, EC2, S3), GCP, Azure
Model performance tracking, drift detection
Jenkins, GitHub Actions, automated testing

Nice To Haves

15+ years of IT Network Engineer of Software Developer/Software Developer in Test experience
Master's degree in related field (Computer Science, Cybersecurity, Computer Engineering, etc.)
Advanced ML: Ensemble methods, gradient boosting (XGBoost, LightGBM, CatBoost)
Deep Learning: CNNs, RNNs, LSTMs, Transformers, attention mechanisms
Specialized Areas: NLP, Computer Vision, Time Series, Recommender Systems

Responsibilities

Develop and maintain AI/ML pipelines supporting model training, validation, and deployment.
Automate AI workflows to ensure reproducibility, scalability, and efficiency.
Integrate AI models into production systems and mission workflows with high reliability.
Collaborate with AI Model Engineers, Data Engineers, Cloud Architects, and ISSE to align AI/ML workflows with infrastructure and security requirements.
Implement monitoring solutions for AI model performance, resource utilization, and operational health.
Evaluate and adopt emerging tools and frameworks to enhance AI/ML pipeline capabilities.