Data Engineer with GEN AI
We are looking for a Data Engineer with strong Generative AI exposure to design, build, and maintain scalable data pipelines and data platforms that power AI/ML and GenAI applications. The ideal candidate should have strong experience in modern data engineering tools along with hands-on Python development and API frameworks.Role: Data Engineer with strong Generative AILocation: New York City, NYKey ResponsibilitiesDesign, build, and maintain scalable data pipelines using modern data engineering tools.Develop and manage data transformation workflows using dbt and Dagster.Build robust data models and pipelines supporting analytics and AI workloads.Develop backend services and APIs using Python and FastAPI to enable data access for AI applications.Work with PostgreSQL and SQL to design efficient schemas and optimize queries.Process and analyze data using Pandas and Python-based data frameworks.Integrate structured and unstructured data pipelines to support Generative AI applications.Build AI agents using LangGraphImplement RAG pipelines and Text-to-SQL systemsIntegrate AI capabilities into enterprise platformsCollaborate with AI/ML engineers to enable LLM-powered applications and data pipelines.Implement data quality, monitoring, and performance optimization practices.Participate in architecture design discussions and contribute to scalable data platform design. Required SkillsStrong programming experience in Python.Experience working with Dagster for orchestration.Hands-on experience with dbt for data transformation and modeling.Advanced SQL skills and experience with PostgreSQL.Experience working with Pandas for data processing.Experience building APIs using FastAPI.Solid understanding of data pipeline design and ETL/ELT processes.Familiarity with building data infrastructure for AI/ML or GenAI use cases.Good to HaveExperience designing Star Schema / Dimensional Data Models.Familiarity with Medallion Architecture (Bronze, Silver, Gold layers).Experience with Generative AI frameworks or LLM integrations.Knowledge of RAG pipelines, vector databases, or embedding workflows.Exposure to cloud platforms such as AWS / GCP / Azure. Vetting Process1 hour technical discussion (Live coding Python)30 minutes Technical Discussion30 minutes Delivery ConnectIn person interview with Customer at NYC, NY