About this opportunity
KADIT Innovations lists this Senior Data AI Engineer opportunity in northern, Kentucky. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Design, develop, and maintain scalable, reliable, and cost-efficient data pipelines and data platforms on GCP (BigQuery, Dataflow, Cloud Storage, Pub/Sub, Composer, Dataproc, etc.)
Architect and optimize large-scale data warehouses and analytical solutions using BigQuery, focusing on performance, cost optimization, and data quality.
Build and operationalize end-to-end AI/ML solutions – from data preparation and feature engineering to model training, evaluation, deployment, and monitoring.
Collaborate with data scientists, product teams, and business stakeholders to translate business requirements into robust data and AI solutions.
Implement best practices for data governance, data quality, security, and compliance across the data platform.
Develop CI/CD pipelines for data and ML workflows (using tools like Cloud Build, Vertex AI Pipelines, Terraform, etc.).
Monitor, troubleshoot, and continuously improve data pipelines and AI model performance in production.
Mentor junior engineers and contribute to technical decision-making and architecture reviews.
Stay updated with the latest GCP services, AI/ML advancements, and industry best practices, and proactively recommend improvements.
Required Skills
5–8 years of overall experience in Data Engineering
Strong hands-on experience with GCP Data Platform and BigQuery
2–3 years of practical AI/ML experience (must have)
Proficiency in Python and SQL.
Experience with ETL/ELT pipelines, data modeling, and large-scale data processing
Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn) and MLOps practices
Strong problem-solving and system design skills.
Nice to Have Skills
Experience with Vertex AI, AutoML, or other GCP AI/ML services
Knowledge of Apache Spark, Airflow/Composer, Kafka/Pub/Sub
Experience with Infrastructure as Code (Terraform / Deployment Manager)
Familiarity with containerization (Docker, Kubernetes/GKE)
Exposure to real-time streaming pipelines and event-driven architectures
Experience in building RAG systems, LLM applications, or Generative AI solutions
Previous experience in mentoring or leading small technical teams
Certifications: Google Cloud Professional Data Engineer / Machine Learning Engineer
Educational Requirements
Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, or a related field.
Equivalent practical experience will also be considered.
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Who can apply
Review the original listing for work authorization, qualifications and employer requirements.