Senior Data Scientist & DataOps Engineer: AI Pipelines

bit schulungscenter GmbH; Miami (FL); Hybrid; USD 140,000 - 210,000; Full time

Job summary

Kmeleon, a Miami-based consulting firm, seeks a Data Scientist / DataOps Engineer to design and maintain scalable data solutions across Azure (primary), AWS, and GCP. You will build data pipelines, DataOps workflows, and ML/AI pipelines in production, enabling robust analytics and experimentation.

The role requires 3+ years in DataOps/Engineering/Science, strong Python, containerization with Kubernetes, and IaC tools.

Qualifications

• 3+ years of hands-on experience in DataOps, Data Engineering, or Data Science roles, with a primary focus on Azure services (Azure Data Factory, Azure Synapse, etc.).
• Working knowledge of AWS (Glue, Redshift, EMR) and GCP (BigQuery, Dataflow) is highly desirable.
• Proficiency in Python for data analysis, ML model development, and scripting.
• Experience with containerization and orchestration (Kubernetes or similar) to manage scalable data processing and model deployments.
• A strong foundation in automation and CI/CD principles, particularly in the context of data pipelines.
• Familiarity with infrastructure as code tools (Terraform, Biceps, ARM templates) to automate provisioning and manage resources.
• Expertise in SQL and NoSQL databases, with hands-on experience in designing data warehouses and data lakes.
• Experience with LLM/advanced AI/ML model deployment and tuning for production environments is a plus.

Responsibilities

• Design and optimize data pipelines on Azure, AWS, and GCP, ensuring efficient ingestion, transformation, and storage of large-scale datasets.
• Develop and maintain DataOps workflows, integrating with CI/CD pipelines for end-to-end automation of data processes.
• Build scalable ML/AI pipelines that support training, validation, and deployment of machine learning models in production environments.
• Collaborate on analytics solutions, assisting in data modeling, statistical analysis, and advanced AI/ML experimentation.
• Implement robust data security and governance strategies, ensuring compliance with industry standards and best practices.
• Troubleshoot and optimize performance across various data systems, identifying areas for continuous improvement in our architecture.

Skills

DataOps

Data Engineering

Data Science

Python

CI/CD

Terraform

Kubernetes

SQL

NoSQL

BigQuery

Tools

Azure Data Factory

Azure Synapse

AWS Glue

AWS Redshift

GCP Dataflow

GCP BigQuery

Kubernetes

Job description

Kmeleon, a Miami-based consulting firm, seeks a Data Scientist / DataOps Engineer to design and maintain scalable data solutions across Azure (primary), AWS, and GCP. You will build data pipelines, DataOps workflows, and ML/AI pipelines in production, enabling robust analytics and experimentation.

The role requires 3+ years in DataOps/Engineering/Science, strong Python, containerization with Kubernetes, and IaC tools.