Generative AI Engineer

Summary

Join our innovative team as a Generative AI Engineer and design, maintain cutting-edge data pipelines for actionable insights. Collaborate with data analysts and business stakeholders to enhance operational efficiency and customer satisfaction through advanced data engineering and AI-driven solutions. This remote role (US-based, excluding CA and HI, EST hours) is contract-to-hire, offering the opportunity to make a meaningful impact. You will build and maintain ETL pipelines, create data models, automate data ingestion and transformation, implement data quality checks, optimize pipeline performance, and collaborate with various teams. The role requires 3–5 years of data engineering experience, proficiency in SQL, Python, and Java, expertise with data integration tools, and knowledge of databases, cloud platforms, and big data technologies. A Bachelor’s degree in a related field is also required.

Requirements

• 3–5 years in data engineering, with strong expertise in ETL processes, data warehousing, and contact center AI/ML applications
• Proficiency in SQL, Python (for ETL, data integration, and modeling), and Java
• Expertise with data integration tools (e.g., Informatica, Talend, or Apache Airflow)
• Strong knowledge of relational and NoSQL databases, cloud platforms (AWS or Google Cloud), and big data technologies (Hadoop, Spark)
• Experience with data manipulation tools (e.g., Pandas) and version control systems
• Familiarity with AI/ML fundamentals, model deployment, and data preparation
• Understanding of data privacy, regulatory compliance, and customer feedback measurement in AI projects
• Bachelor’s degree in Computer Science, Engineering, or a related field

Responsibilities

• Build and maintain robust ETL (Extract, Transform, Load) pipelines to process data from diverse sources, including CRM and call center systems, ensuring seamless integration into data warehouses and lakes
• Create dimensional and analytical data models to support business reporting and analytics needs, accurately reflecting business domains
• Automate data ingestion and transformation processes, resolving inconsistencies to ensure data integrity across multiple sources
• Implement checks to monitor and maintain data accuracy and completeness, proactively addressing quality issues
• Enhance data pipelines and queries for efficient processing, ensuring timely delivery of insights
• Partner with data analysts, business stakeholders, and data science teams to translate business needs into technical solutions, supporting advanced analytics and machine learning initiatives