We are looking for a hands-on CTO Operations Engineer to join the CTO organization at our company. In this role, you will focus on improving deployment pipelines and internal tools by digging into logs, finding recurring issues, and reducing manual work.
The role centers on hands-on technical development, including building internal tools, data pipelines, and AI workflows, alongside technical operations such as setting KPIs, dynamic dashboards, and cross-domain alignment.
Responsibilities
Work closely with domain leads to identify technical bottlenecks across product lines and engineering workflows.
Help build and maintain the Unified Maturity Table, defining technical parameters such as workflows, support packages, and other metrics along with their KPIs that conclude a domain readiness and maturity.
Partner with the team to refine internal AI agents by measuring the performance of their answers against human expert interventions.
Use AI and cluster analysis on support ticket descriptions to identify recurring patterns, then design and implement automated workflows to streamline resolution.
Take part in driving the departments "operating system" by facilitating design meetings, managing cross-domain syncs, and ensuring the documentation and support packages required for daily execution are current and actionable.
Define and implement different thresholds by analyzing support ticket complexity, resolution times, and domain-specific friction.
Transition static maturity reports into live, dynamic dashboards with trend lines.
Lead design meetings to establish cross-functional standards for domain maturity and system health alerts.
Requirements: 3 - 6 years of hands-on experience as a Software Engineer, Data Engineer, SRE, or Solutions Engineer.
B.Sc. in Computer Science, Software Engineering, Mathematics, or a related scientific field.
Proficiency in querying and analyzing large datasets with Python and advanced SQL on platforms like Snowflake or BigQuery to diagnose system bottlenecks and deployment failures.
Experience with modern data modeling tools and building reliable data pipelines.
Daily hands-on experience with AI-assisted development tools like Cursor or Claude Code, alongside strong prompt engineering skills.
A proven track record of taking manual, "friction-heavy" processes and building automated, scalable engineering solutions.
Preferred Qualifications
Experience with AI agent architectures, RAG concepts, and auditing AI model outputs for accuracy and reliability.
Familiarity with cloud and infrastructure environments such as AWS, GCP, Linux, Docker, Git, and Terraform.
Strong ability to translate complex technical friction into structured roadmaps, establish metrics, and drive execution end to end.
This position is open to all candidates.