Senior AI Platform Developer

Title:
Senior AI Platform Developer

Job Type: Contract

Duration:
Long-term

Location:
Herndon, VA - Onsite

Visa:
No Sponsorship Required

We are seeking a Senior AI Platform Developer to help design, build, and scale advanced AI/ML solutions within a modern engineering environment. This role requires strong hands-on development skills combined with a solid understanding of machine learning systems deployed at scale.

• Design, develop, and optimize AI/ML models and platform components for production use.
• Build scalable data pipelines and model workflows supporting training, evaluation, and deployment.
• Collaborate with cross-functional engineering and data teams to integrate AI capabilities into broader systems.
• Contribute to model performance optimization, reliability, and maintainability.
• Support deployment, monitoring, and lifecycle management of AI models in cloud environments.

• Programming: Strong proficiency in Python
  ; experience with Java, C++, or R is a plus.
• Machine Learning & Deep Learning: Solid understanding of supervised, unsupervised, and reinforcement learning, along with neural network architectures (e.g., CNNs, RNNs).
• AI/ML Frameworks: Hands-on experience with frameworks such as Tensor Flow, PyTorch, Keras, and scikit-learn
  .
• Data Engineering & Analysis:
  
  Experience with data ingestion, preprocessing, feature engineering, and working with SQL and No
  
  SQL databases.
• Big Data Technologies: Familiarity with distributed data processing tools such as Apache Spark and search/analytics platforms.
• Mathematics & Statistics: Strong foundation in linear algebra, probability, statistics, and optimization techniques.
• Natural Language Processing (NLP):
  
  Experience with NLP concepts and libraries such as spaCy, NLTK, or Transformer-based models
  .
• Cloud & MLOps: Exposure to cloud platforms (
  AWS, Azure, or GCP
  ) and MLOps practices including CI/CD for ML, model versioning, and monitoring.

• Experience building reusable AI platforms or shared ML services.
• Exposure to large-scale, production-grade ML systems.
• Familiarity with model governance, performance tracking, and optimization techniques.