Description

Design, architect, and oversee the deployment of machine learning systems and data-intensive applications.

This role is on-site.

Responsibilities

• Design ML model training, validation, and hyper-parameter search pipelines.
• Develop distributed applications on-prem and in the cloud to serve models, analytics, and visualizations.
• Implement production deployment and model monitoring to ensure stable performance.
• Lead data science and engineering teams within a distributed agile framework.
• Contribute to the adoption of CI/CD, DataOps, and MLOps practices.

Required Skills

• 12+ years of experience in the design, architecture, and operationalization of AI/ML models.
• Expertise in Python, (Py)Torch, (Py)Spark, Advanced SQL, and Shell scripting.
• Deep experience with Big Data ecosystems including Hadoop, Spark, Kafka, and MongoDB.
• Proficiency with cloud platforms and tools such as Databricks, MLFlow, GitHub, AKS, and Snowflake.
• Proven ability to architect and implement data-intensive applications.
• Experience with both SQL and NoSQL datastores and stream data processing.
• Strong background in end-to-end applied machine learning lifecycles.
• Ability to develop libraries for data and model monitoring and control.

Preferred Skills

• Experience with distributed agile frameworks.

Key Skills

Education

Any Graduate