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.

Key Responsibilities

• 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.
  
  

Required Skills & Experience

• 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  TensorFlow, PyTorch, Keras, and scikit-learn .

• Data Engineering & Analysis:  Experience with data ingestion, preprocessing, feature engineering, and working with  SQL and NoSQL  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.

Nice to Have

• 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.