Programmers.io

AI/ML Data Engineer with Advanced Analytics

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI/ML Data Engineer with Advanced Analytics in Austin, TX (Hybrid). Contract length exceeds 6 months, with a pay rate of "unknown." Requires 5+ years in relevant technologies, expertise in Snowflake, Python, and deploying AI models on UI.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
-
🗓️ - Date
August 12, 2026
🕒 - Duration
More than 6 months
-
🏝️ - Location
Hybrid
-
📄 - Contract
W2 Contractor
-
🔒 - Security
Unknown
-
📍 - Location detailed
Austin, TX
-
🧠 - Skills detailed
#Automation #Streamlit #Process Automation #AI (Artificial Intelligence) #ML (Machine Learning) #Python #Monitoring #Snowflake #Data Science #React #Data Pipeline #Deployment #Data Engineering #"ETL (Extract #Transform #Load)" #Tableau
Role description
Role: AI/ML Data engineer with Advanced Analytics Location: Austin, TX - Hybrid Job Type: Fulltime / W2 / C2C AI engineer who has experience in deploying models and hosting them on UI like streamline, react and monitoring them and making any necessary changes. Work with business users to troubleshoot the issues. Skills: • Snowflake and Data pipelines • Python • AI/ML and Advanced Analytics • Building and deployment AI agents on UI (Streamlit, react) • Tableau Experience: • 5+ years of relevant experience in the required technologies and 10+ years of overall IT experience. • The individual will work Three Days Per Week from the Austin office. • Responsibilities include developing ETL pipelines, designing Snowflake data structures, creating Tableau reports, and identifying process automation opportunities using Python. • The candidate will work closely with the Operations team, as well as data scientists and software engineers, on Python-based applications, machine learning models, and optimization solutions that support both development and operational activities. • The candidate will also collaborate with business teams to understand reporting requirements and build the corresponding data science pipelines.