Description

You will architect and implement enterprise-grade data and AI/ML solutions.

Responsibilities

• Design and optimize scalable, secure Databricks-based AI/ML architectures for advanced analytics and ML workloads.
• Implement robust integrations between diverse internal and external data sources for efficient data ingestion and transformation.
• Develop and enforce a ModelOps strategy covering model lifecycle, governance, versioning, and deployment at scale.
• Enable self-service by building frameworks and tools that allow data scientists to train and deploy models independently.
• Serve as a trusted advisor, leading workshops and POCs to solve complex data and AI challenges.

Required Skills

• 5+ years architecting and implementing enterprise data and AI/ML solutions, preferably with Databricks, Apache Spark, and AWS, Azure, or GCP.
• Deep understanding of Data Lakehouse architectures, ETL/ELT pipelines, data governance, and security best practices.
• Demonstrated experience in developing and implementing ModelOps strategies.
• Proficiency with core DevOps tools including Terraform, Git, and Python.
• Experience architecting solutions on major cloud platforms (AWS, Azure, GCP).
• Expertise in building and optimizing ETL/ELT Pipelines.

Key Skills

Education

Bachelor's or Master's degree in Computer Science