Description

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

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.

Responsibilities

• Design and manage end-to-end MLOps architectures within AWS GovCloud and FedRAMP environments.
• 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.

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.

Preferred Skills

• Experience with AIOps and Flask-based deployments.

Key Skills

Education

Any Graduate