Top Tech Jobs & Startup Jobs

Lead/Manager for GenAI and Agentic AI engineering to design, build, evaluate, and productionize LLM-based systems. Responsibilities include model selection, prompting and RAG design, evaluation plans and metrics, experiment management, failure-mode analysis, and delivering engineering-ready artifacts (prompts, RAG configs, evaluation harnesses) while collaborating with engineering, data, and product teams.

Design and implement end-to-end enterprise data architectures; build, maintain, and optimize data pipelines with Python, SQL, and Spark; manage large-scale storage (Iceberg, Hadoop, HDFS); develop dbt models; implement ingestion from APIs and on-prem systems; collaborate with stakeholders and document processes to improve data quality and engineering practices.

The Lead Data Scientist will design, develop, and deploy Machine Learning models, collaborate with teams to integrate solutions, and implement MLOps practices while providing mentorship and communicating findings effectively.

The Data Quality Engineer is responsible for ensuring data quality and reliability across data platforms, validating pipelines, implementing automated checks, and collaborating with teams to deliver accurate data assets.

The Data Quality Engineer ensures data quality and consistency across Azure and Databricks platforms by validating data pipelines, implementing automated checks, and collaborating with teams to confirm reliable data assets.

The Data Quality Engineer will ensure data quality and reliability across data platforms, validating pipelines and implementing automated checks, while collaborating with data engineering and business teams.

The Senior AP Analyst manages accounts payable operations, including vendor payments, reconciliations, and process improvements for multi-entity transactions. Responsible for AP cycle management, corporate card reconciliations, and cross-departmental communications.

Design, build, and maintain scalable Snowflake-based data ingestion and ETL pipelines. Transform, normalize, and validate large datasets using Python and SQL, implement data quality checks and monitoring, support file ingestion workflows, document processes, and collaborate with stakeholders to deliver reliable datasets for analytics and business workflows.

The Data Quality Engineer will implement a data quality framework, validate data pipelines, and collaborate with Data Engineering to ensure accurate data across platforms.

Lead design and implement Snowflake-based, scalable ELT ingestion pipelines and data validation frameworks. Enforce Medallion Architecture, ensure data quality and governance, optimize AWS data infrastructure, mentor engineers, and manage client-facing technical delivery.