Description

You will design and manage end-to-end MLOps architectures within AWS GovCloud and FedRAMP environments.

Responsibilities

• Build and maintain ML model operationalization pipelines in production using Databricks and Unity Catalog.
• Develop machine learning solutions involving classification, clustering, optimization, NLP, and deep learning.
• Implement GenAI and LLMOps workflows using MosaicAI and serverless solutions.
• Manage distributed computing workloads using Spark and the Kubernetes ecosystem.
• Integrate CI/CD and DevOps processes into the machine learning lifecycle.

Required Skills

• 8 to 10 years of experience in data engineering, machine learning, or data science.
• 4 to 6 years of experience in AWS GovCloud, Export Control, or FedRAMP environments.
• 4 to 6 years of expertise in Python and PySpark coding.
• 4 to 6 years of experience with Databricks (Azure or AWS), Unity Catalog, and Lakehouse monitoring.
• 4 to 6 years of experience with ML frameworks including Keras, TensorFlow, PyTorch, and HuggingFace Transformers.
• 4 to 6 years of experience with distributed computing frameworks like Spark and Kubernetes.
• 4 to 6 years of experience with CI/CD and DevOps processes.
• Strong proficiency in Python, Flask, and Django.
• Experience with GenAI, LLMOps, and NLP.
• Knowledge of scikit-learn, statistics, and machine learning/deep learning algorithms.
• Ability to create BI reports and data visualizations.

Preferred Skills

• Experience with AIOps and Flask-based deployments.

Key Skills

Education

Any Graduate