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Senior, niche GenAI plus data engineering skillset reduces general applicant density despite metro location.
Skills are transferable across industries but require strong domain-specific data and GenAI experience.
Many mandatory, specific technologies and production-grade AI requirements increase filter rigidity significantly.
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Design, build, and maintain end-to-end batch and real-time data pipelines and cloud data warehouse/lakehouse architectures using Python, PySpark, dbt, Airflow, Snowflake, BigQuery, and Delta Lake.
Develop and deploy production-grade AI systems including Retrieval-Augmented Generation (RAG) pipelines, distributed agent-based systems, and LLM evaluation/automation integrated with enterprise data platforms.
Manage deployment and monitoring of data and AI systems on cloud platforms (Azure/AWS) with Kubernetes, CI/CD pipelines, FastAPI microservices, and caching to ensure production reliability and low latency.
Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or related technical discipline.
Demonstrable work experience in data engineering or AI engineering with increasing scope and ownership; specific duration not explicitly mentioned.
Strong proficiency in Python, SQL, dbt, Airflow, PySpark/Databricks, cloud data warehouses (Snowflake, BigQuery, Redshift), LLM frameworks (LangChain/LangGraph), and experience with Azure or AWS cloud platforms including Kubernetes and CI/CD.
Portfolio of delivered production pipelines, warehouse implementations, or AI applications preferred; dbt Analytics Engineering Certification and cloud certifications valued but not mandatory.
Experienced in both scalable data infrastructure and production-grade AI system development with focus on robust, enterprise-grade solutions.
Practical expertise in multi-technology environments combining data engineering rigor and advanced AI/GenAI engineering, capable of working across ingestion, transformation, reasoning, and deployment layers.
Able to own full stack data-to-intelligence workflows and deployments with proven ability to integrate diverse data sources and AI frameworks in cloud-native contexts.