Machine Learning Engineer Contract

Immediate need for a talented Machine Learning Engineer. This is a 06+ Months contract opportunity with long-term potential and is located in Austin TX (Hybrid). Please review the job description below and contact me ASAP if you are interested.

Job ID: 24-21129

Pay Range: $75 – $77/hour. Employee benefits include, but are not limited to, health insurance (medical, dental, vision), 401(k) plan, and paid sick leave (depending on work location).

Key Requirements and Technology Experience:

• Machine Learning Engineer with 7+ years of experience in designing, building, and maintaining machine learning models and pipelines.
• Proven ability to work with a variety of data sources, including structured and unstructured data.
• Strong experience in Python (MLlib, TensorFlow, and PyTorch).
• Machine learning libraries: TensorFlow, PyTorch, Scikit-learn, Deploying and optimizing different pipelines that support various data science processes.
• Establish and set up model life cycle management with tools like MLFlow, etc
• Developing and deploying Spark/Databricks jobs with enterprise tool stack including Jenkins, GitHub Actions
• Deployment utilizing containerization solutions like Docker and Kubernetes
• Experience with AWS cloud services and running Apache Spark applications.
• Experience with API development leveraging Fast API / Flask
• Must have industry experience in building Forecasting models

Our client is a leading Insurance organization and we are currently interviewing to fill this and other similar contract positions. If you are interested in this position, please apply online for immediate consideration.

Pyramid Consulting, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, colour, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.