AppOmni

Data Scientist

• Design and implement scalable batch and real-time data processing systems across large and complex datasets.
• Build and optimize ETL and streaming data pipelines using modern GCP big data technologies.
• Support development decisions around model choices, data architecture, data modeling, pipeline orchestration, analytics infrastructure, and production systems.
• Develop statistical models and analytics capabilities that support product intelligence and operational insights.
• Design and maintain production-grade data workflows using technologies such as Airflow, Dataflow, PubSub, and PySpark.
• Contribute across multiple areas of the data ecosystem, including data engineering, monitoring and governance, visualization, and analytics tooling.
• Establish monitoring, observability, and governance practices for data quality, pipeline reliability, and production health.
• Partner closely with Engineering to operationalize scalable data infrastructure and analytics systems.
• Collaborate with Product to shape intelligent, data-driven product capabilities and user experiences.
• Act as a thought partner across data engineering, analytics, infrastructure, and applied modeling initiatives.
• Help evolve internal tooling and frameworks that improve scalability, reliability, and operational efficiency across the platform.

Senior Data Scientist

Full-stack Engineer

• Design, build, and maintain backend services and APIs to support our GenAI product using Python and FastAPI, ensuring they are scalable, reliable, and secure.
• Develop and operate LLM-powered applications and agentic workflows using LangChain, LangGraph, and LangSmith, including tracing, evaluation, and observability.
• Develop and manage infrastructure using Terraform, applying infrastructure-as-code (IaC) practices across backend and ML resources.
• Build and maintain monitoring and alerting systems to surface service and API health issues early, ensuring reliability and performance.
• Handle large datasets to ensure data is processed, ready for model context, and collaborate in the platform data pipeline.
• Evaluate and select appropriate tools and technologies and define the overall technical strategy.
• Assess and interpret data to gain insights, especially for testing and monitoring purposes.
• Build and maintain CI/CD pipelines, including infrastructure-as-code (IaC), and automate deployment and monitoring across backend services and ML workflows.
• Continuously improve and automate monitoring and alerts processes.
• Optimize infrastructure and services for performance, scalability, reliability, and cost efficiency, and troubleshoot issues across deployment and runtime.
• Troubleshoot and resolve issues related to pipeline deployment and performance.
• Contribute to the development of internal engineering standards and documentation.
• Advocate for engineering best practices and promote a culture of continuous improvement also mentoring and guiding others in the company in this topic.
• Understand the requirements of stakeholders and translate them into technical solutions.
• Develop robust model monitoring and alerting systems to ensure model performance and identify issues.
• Support our mission and vision and embody and demonstrate AppOmni core values of Trust, Transparency, Quality, Customer Focus, and Delivery. Comply with all company policies and governing laws and regulations.

Lead Software Engineer

Technical Account Manager

• Manage and serve as the primary point of contact for all post-sales technical and functional needs for AppOmni’s top enterprise customers through strong technical acumen, in-depth product knowledge, relationship-building, strategic planning, business value articulation, and execution.
• Be consultative and be viewed as a technical SME by exhibiting a complete understanding of customer technical goals and objectives. Develop a trusted advisor relationship with customer champions, sponsors, and technical teams to drive product adoption and ensure they are using AppOmni to achieve full business value.
• Ensure that a deployment and adoption plan is in place with each customer in order to facilitate the customer achieving their stated success criteria. Work closely with AppOmni’s Customer Success Manager (CSM) team to monitor and identify adoption and utilization trends, provide recommendations based on customers' needs, risks, and strategy.
• Introduce, demonstrate, and implement new products and features as they become available.
• Develop expertise in AppOmni’s API capabilities to help customers augment UI-based functionality with script-based approaches to unlock value.
• Work closely with the Product and Engineering teams to define and influence SaaS application support roadmap and augmentation beyond the AppOmni Developer Platform.
• Increase customer retention by assisting regular health check meetings for tactical items, and assisting with strategic business reviews for alignment of objectives and outcomes
• Help the customer document their technical success criteria and strategy and partner with the CSM and customer teams to ensure adherence to the strategy; work with the customer to update technical success criteria if/as customer priorities shift.
• Define and document the customer’s end-state AppOmni architecture and work with the customer to develop a roadmap and strategy for achieving the desired end-state architecture.
• Track accounts to identify customer risk and work actively to eliminate that risk; partner with the CSM by contributing to the renewal strategy and work closely with the AppOmni Renewals Manager to influence the successful execution of renewals.
• Be the technical voice of the customer to our internal stakeholders (engineering, product, sales, and executive leadership teams) and provide internal feedback on how AppOmni can better serve our enterprise customers.
• Work closely with Product and Engineering on identification and tracking of product improvement requests, troubleshooting, and bugs.
• Evangelize customer success stories with the AppOmni Marketing team.
• Above all, put customer’s needs first and demonstrate customer obsession.

• Design and implement scalable batch and real-time data processing systems across large and complex datasets.
• Build and optimize ETL and streaming data pipelines using modern GCP big data technologies.
• Lead development decisions around model choices, data architecture, data modeling, pipeline orchestration, analytics infrastructure, and production systems.
• Develop statistical models and analytics capabilities that support product intelligence and operational insights.
• Design and maintain production-grade data workflows using technologies such as Airflow, Dataflow, PubSub, and PySpark.
• Contribute across multiple areas of the data ecosystem, including data engineering, monitoring and governance, visualization, and analytics tooling.
• Establish monitoring, observability, and governance practices for data quality, pipeline reliability, and production health.
• Partner closely with Engineering to operationalize scalable data infrastructure and analytics systems.
• Collaborate with Product to shape intelligent, data-driven product capabilities and user experiences.
• Act as a technical leader and thought partner across data engineering, analytics, infrastructure, and applied modeling initiatives.
• Help evolve internal tooling and frameworks that improve scalability, reliability, and operational efficiency across the platform.

Senior Data Scientist

Lead Software Engineer

Technical Account Manager

Lead Data Scientist