MLOps Engineer

September 2, 2024

Accenture

Timișoara

OTHER

About us

Accenture Industry X, part of Accenture, helps businesses thrive in the digital era by combining data and digital capabilities. Join us to experience how we deliver 360 value and collaborate with exceptional people, cutting-edge technology, and leading companies across various industries, making a significant impact worldwide.

How Will You Make an Impact?
Automation: Streamline repetitive tasks such as data preprocessing, model training, and deployment through automation.
Scalability: Ensure that machine learning models can scale effectively to handle large volumes of data and high traffic.
Reproducibility: Achieve consistent and reproducible results by tracking experiments, code, data, and model versions.
Monitoring: Continuously monitor model performance and data quality to promptly detect and address any issues.
Collaboration: Enhance collaboration between data scientists, ML engineers, and operations teams to ensure seamless workflows.

What Makes the Difference

1BI Platforms
Tableau: For creating interactive and shareable dashboards.
Power BI: For data visualization and business analytics.
Qlik View Qlik Sense: For self-service data visualization and discovery.
Looker: For data exploration and business insights.
AWS - Quick sight
2. ETL Tools
Talend: For data integration and ETL processes.
Informatica: For enterprise data integration.
Apache Nifi: For data flow automation and ETL.
Microsoft SSIS SQL Server Integration Services: For data migration and ETL.
3. Data Warehousing
Amazon Redshift: For cloud-based data warehousing.
Google Big Query: For large scale data analytics.
Snowflake For cloud data warehousing.
Microsoft Azure Synapse Analytics For big data analytics and data warehousing.

4. Databases
My SQL For relational database management.
Postgre SQL For advanced relational database management.
SQL Server For enterprise database management.
Oracle Database For large scale database management.
5. Data Visualization

D3.js :For custom data visualizations.
Matplotlib Seaborn Python: For data visualization in Python.
ggplot2: R For data visualization in R.
6. Data Preparation
Alteryx: For data blending and advanced analytics.
Trifacta: For data wrangling and preparation.
7. Cloud Platforms
AWS Amazon Web Services: For cloud based data storage and analytics e.g., AWS Glue, AWS Quick Sight .
Microsoft Azure: For cloud services and analytics e.g., Azure Data Factory, Azure Analysis Services.
Google Cloud Platform: For cloud computing and data analytics e.g., Google Data Studio, Google Cloud Dataflow .
8. Collaboration and Version Control
Git: For version control and collaboration.
JIRA: For project management and issue tracking.
9. Advanced Analytics
Python For data analysis and machine learning.
R For statistical analysis and data visualization.
SAS For advanced analytics and statistical analysis.