AI Engineer

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Pittsburgh, Pennsylvania / $75 - $85 hour

INFO

Salary
SALARY:

$75 - $85

Location

LOCATION

Pittsburgh, Pennsylvania

Job Type
JOB TYPE

Contract

Responsibilities:

  • Design, develop, and deploy AI/ML models for applications such as NLP, predictive analytics, and information retrieval.

  • Preprocess and structure large datasets, ensuring quality, consistency, and compliance with data handling standards.

  • Implement machine learning algorithms and techniques to improve model performance and efficiency.

  • Collaborate with engineers, product managers, and business stakeholders to align technical work with organizational goals.

  • Continuously test, evaluate, and refine models, incorporating new data and feedback.

  • Deploy models into production environments with a focus on scalability, reliability, and security.

  • Stay current with developments in AI/ML research and apply emerging techniques where practical.

  • Document models, pipelines, and processes, and communicate results to both technical and non-technical audiences.

Requirements:

  • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Mathematics, or related field (PhD a plus).

  • 2+ years of hands-on experience in machine learning, deep learning, or AI applications.

  • Proficiency with machine learning frameworks and libraries (e.g., PyTorch, TensorFlow, Scikit-learn, etc.).

  • Strong programming skills in Python (Java or C++ experience also valuable).

  • Solid grounding in algorithms, statistics, probability, and linear algebra.

  • Familiarity with cloud platforms (AWS, Azure, or GCP) for deploying ML models.

  • Experience with version control and collaborative development (Git preferred).

  • Strong problem-solving skills and ability to communicate technical concepts clearly.

Preferred Qualifications:

  • Experience working with sensitive or regulated data.

  • Background in applying NLP or generative AI techniques to real-world use cases.

  • Familiarity with containerization/orchestration (Docker, Kubernetes).

  • Exposure to big data tools (Spark, Hadoop) a plus.

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