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.

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