Data Engineer – AI & Machine Learning

We have deep expertise in Data Engineering, AI, and Machine Learning and a proven track record of connecting exceptional candidates with industry-leading clients nationwide. We are currently seeking a skilled Data Engineer with strong AI and ML experience for immediate placement across our active client engagements. Roles & Responsibilitie • sDesign, build, and maintain scalable batch and real-time data pipelines across cloud environment • sArchitect and manage data lake, data warehouse, and lakehouse solutions using Snowflake, Databricks, and Delta Lak • eDevelop ETL and ELT workflows to ingest, transform, and deliver reliable data to ML models, dashboards, and business application • sBuild and maintain feature engineering pipelines and feature stores to support ML model training and inferenc • eCollaborate with data scientists and ML engineers to design data infrastructure supporting model development and production deploymen • tImplement real-time streaming pipelines using Apache Kafka, Spark Streaming, or Apache Flin • kBuild vector data pipelines and embedding workflows to support LLM-powered applications and RAG system • sImplement data quality frameworks, validation checks, and observability tooling across all data asset • sEnforce data governance practices including lineage tracking, metadata management, and data privacy complianc • eOptimize data models and query performance for large-scale analytical and ML workload • sImplement CI/CD pipelines for data infrastructure using infrastructure-as-code and DevOps best practice s Required Qualificatio • ns4–6 years of experience in data engineering with strong focus on AI and ML data infrastructu • re2+ years of experience integrating AI and ML workflows into data pipelines and platfor • msStrong proficiency in Python and SQL for pipeline development and data transformati • onHands-on experience with Apache Spark and PySpark for large-scale distributed data processi • ngProven expertise with Snowflake, Databricks, BigQuery, or Redshift for data warehousing and lakehouse architectu • reExperience with data orchestration tools including Apache Airflow, Prefect, or Dagst • erSolid knowledge of real-time streaming using Apache Kafka, Spark Streaming, or AWS Kines • isFamiliarity with ML concepts including feature engineering, model training pipelines, and data preparation for AI use cas • esCloud proficiency on AWS, GCP, or Azure including managed services for storage, compute, and orchestrati • onExperience with containerization and orchestration using Docker and Kubernet • esKnowledge of data governance, data lineage, and data quality best practic • esDegree in Computer Science, Engineering, Mathematics, or equivalent practical experienc e.