Currently tracking 66 active AI roles, down 30% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $130k–$425k (avg $220k).
| Title | Stage | AI score |
|---|---|---|
| Manager, Field Engineering Manager, Field Engineering for Databricks in Latam, leading a team of Solutions Architects to drive technical sales, increase revenue, and ensure customer success with Databricks' platform, which includes generative AI capabilities. The role focuses on hiring, training, and enabling the team, maintaining technical expertise, and fostering cross-functional relationships. | — | 5 |
| Sr Data & AI Technical Solutions Engineer This role focuses on supporting customers in debugging and maintaining stable production data pipelines and AI workflows on the Databricks platform. The engineer will provide initial analysis, troubleshooting, and resolution for data engineering and AI workloads, perform deep dives into code-level analysis, and contribute to product improvements. | Serve | 5 |
| Scale Solutions Engineer |
| — |
| 0 |
| Sr Technical Escalations Manager This role manages critical customer issues and major incidents by coordinating efforts between internal teams (engineering, product management, Customer Success Engineering, Support) and external partners to ensure timely resolution and customer satisfaction. The role requires experience in customer support, escalation, SRE, or incident management, along with skills in distributed big data computing, SQL, data warehousing, ETL, Linux/Unix, networking, and cloud platforms (AWS, Azure, GCP). | — | 0 |
| Staff Technical Solutions Engineer, Platform Seeking a Senior Technical Solutions Engineer with over 10 years of experience to join the Databricks Platform Support team. This hybrid role focuses on providing frontline support for the Databricks Data Intelligence platform, addressing complex technical challenges, and ensuring seamless operation of data solutions. Responsibilities include serving as the primary technical contact for escalated issues, customer interaction, incident management, root cause analysis, collaboration with engineering and product teams, documentation, performance monitoring, platform upgrades, and driving platform improvements. | — | 0 |