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.

Key Responsibilities

• 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.

Required Skills & Experience

Programming & Development

• Strong proficiency in Python .

• Working knowledge of Java, C++, or R is a plus.

• Experience writing clean, maintainable, and production-ready code.

Machine Learning & Deep Learning

• Solid understanding of supervised, unsupervised, and reinforcement learning .

• Hands-on experience with neural network architectures such as CNNs and RNNs .

AI/ML Frameworks

• Practical experience with TensorFlow, PyTorch, Keras, and scikit-learn .

• Ability to select and apply appropriate models and techniques for real-world use cases.

Data & Big Data Technologies

• Experience with data ingestion, preprocessing, and feature engineering .

• Working knowledge of SQL and NoSQL databases .

• 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 .

Natural Language Processing (NLP)

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

Cloud & MLOps

• Exposure to cloud platforms ( AWS, Azure, or GCP ).

• Understanding of MLOps practices , including model versioning, CI/CD for ML, deployment, and monitoring.

Nice to Have

• 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.