AI Platform Developer – Mid Level

Title:

AI Platform Developer – Mid Level

Job Type: Contract

Duration:
Long-term

Location:
Herndon, VA - Onsite

Visa:
No Sponsorship Required

We are seeking a Mid-Level AI Platform Developer to design, build, and support scalable AI/ML solutions within a modern engineering environment. The role focuses on developing production-ready machine learning components, data pipelines, and model deployment workflows.

• Develop and enhance AI/ML models and platform components for production use.
• Build and maintain data pipelines supporting model training, evaluation, and inference.
• Collaborate with engineering and data teams to integrate AI capabilities into larger systems.
• Optimize model performance, scalability, and reliability.
• Support deployment, monitoring, and lifecycle management of ML models in cloud environments.

• Strong proficiency in Python
  .
• Working knowledge of Java, C++, or R is a plus.
• Experience writing clean, maintainable, and production-ready code.

• Solid understanding of supervised, unsupervised, and reinforcement learning
  .
• Hands-on experience with neural network architectures such as CNNs and RNNs
  .

• Practical experience with Tensor Flow, PyTorch, Keras, and scikit-learn
  .
• Ability to select and apply appropriate models and techniques for real-world use cases.

• Experience with data ingestion, preprocessing, and feature engineering
  .
• Working knowledge of SQL and No
  
  SQL databases
  .
• Familiarity with distributed data processing tools such as Apache Spark and search/analytics platforms.

• Strong foundation in linear algebra, probability, statistics, and optimization
  .

• Experience with NLP concepts and libraries such as spaCy, NLTK, or Transformer-based frameworks
  .

• Exposure to cloud platforms (
  AWS, Azure, or GCP
  ).
• Understanding of MLOps practices
  , including model versioning, CI/CD for ML, deployment, and monitoring.

• Experience contributing to shared AI platforms or reusable ML services.
• Exposure to large-scale or high-availability ML systems.
• Familiarity with model governance, performance tracking, and optimization.