Technical Support Engineer
ChartbeatFull Time
Mid-level (3 to 4 years)
Candidates should have a minimum of 3 years of experience in Data or Software Engineering, with a solid understanding of public cloud technologies, containers, and ML/data infrastructure. Strong Python programming skills are essential, and Go experience is a plus. A customer-centric approach and experience supporting both SMB and enterprise customers with infrastructure products are required. Familiarity with data processing, ML systems, MLOps, and Machine Learning infrastructure components is also necessary. Experience with or strong interest in the latest trends in machine learning and data orchestration fields is expected, along with excellent written and verbal communication skills.
The Support Engineer will resolve, triage, and escalate customer workflow issues by diagnosing platform/infrastructure versus user code problems. They will drive retention and adoption by understanding customer business objectives and technical solutions, and partner with Engineering, Product, and Sales to represent the voice of the customer. Responsibilities include collaborating with the Documentation team to create customer-facing materials, representing customer needs in product design discussions, and educating technical users on Union products. The role also involves meeting support KPIs and SLAs, and maintaining communication with customers regarding their adoption trends and sentiment.
Managed platform for ML and data orchestration
Union.ai offers a managed platform for machine learning (ML) and data pipeline orchestration, with its main product, Flyte, designed to handle complex workflows using Python code. Union Cloud simplifies the deployment and management of Flyte, allowing ML engineers and data scientists to focus on their work without worrying about infrastructure. The company serves a range of clients, including large enterprises, and operates on a subscription-based model for its services. Union.ai's goal is to accelerate ML projects by automating processes involved in model development and deployment, resulting in faster time-to-market.