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.