Database and AI Support Engineer

Accountabilities: Provide advanced technical support for database, AI, cloud, Kubernetes, and infrastructure-related customer issues. Diagnose complex technical problems by analyzing logs, system behavior, configurations, networking conditions, and other available evidence. Investigate database performance and reliability issues involving data modeling, query optimization, indexing, replication, clustering, backups, and performance tuning. Troubleshoot Kubernetes environments, including AKS, EKS, OpenShift, Helm deployments, ingress controllers, service endpoints, and cluster administration workflows. Analyze infrastructure and networking issues involving load balancers, timeouts, DNS, storage, connectivity, and service endpoints. Support environments using AWS, Azure, on-premises infrastructure, Linux, Docker, and related cloud-native technologies. Work with storage technologies such as EBS, gp3, MinIO, and S3-compatible storage. Apply knowledge of LLMs, embeddings, vector search, RAG, prompt engineering, and AI APIs when diagnosing or supporting AI-enabled applications. Use REST APIs, JSON, authentication mechanisms, and scripting with Python, Bash, or similar tools to investigate and resolve issues. Read and understand application code to assist with technical troubleshooting and identify potential product defects. Collaborate with Engineering, Product, QA, and Customer Success teams to escalate issues, communicate findings, and drive effective resolutions. Document solutions, troubleshooting procedures, and technical findings to strengthen internal knowledge bases and improve future support efficiency. Build customer trust through clear communication, thoughtful expectation management, and a structured approach to understanding the underlying issue before troubleshooting. Requirements 5+ years of professional experience in Support Engineering or a closely related technical support role. At least 3 years of hands-on experience working with databases, preferably including ArangoDB, Neo4j, or comparable database technologies. Strong understanding of database fundamentals, including data modeling, query optimization, indexing, clustering, replication, backups, and performance tuning. Strong Kubernetes experience, including platforms such as AKS, EKS, or OpenShift, along with Helm. Solid understanding of cloud infrastructure, particularly AWS and Azure, as well as on-premises environments. Familiarity with storage systems including EBS, gp3, MinIO, and S3-compatible storage. Ability to troubleshoot networking issues involving load balancers, timeouts, DNS, service endpoints, and ingress controllers. Comfortable working with Linux, SSH, jump boxes, and cluster administration workflows. Basic scripting capabilities and familiarity with Python, Bash, or similar scripting languages. Ability to read and understand software code and use that knowledge to support technical investigations. Understanding of AI and RAG concepts, including LLMs, embeddings, vector search, prompt engineering, and AI APIs. Familiarity with REST APIs, JSON, and authentication mechanisms. Strong analytical and critical-thinking skills, with the ability to break complex problems into manageable steps and identify root causes objectively. Excellent communication skills, with the ability to explain complex technical concepts clearly to technical and non-technical audiences. Strong customer orientation, including empathy, expectation management, and the ability to establish trust during challenging technical situations. Collaborative mindset and ability to work effectively with Engineering, Product, QA, Customer Success, and other cross-functional teams. Fluent written and spoken English. Benefits Fully remote position for candidates based in India. Opportunity to work at the intersection of AI, databases, and cloud-native infrastructure. Exposure to cutting-edge technologies supporting enterprise AI applications and intelligent systems. Opportunity to work with experienced engineering, product, and business professionals. Cross-functional collaboration with Engineering, Product, QA, and Customer Success teams. Opportunity to solve complex technical challenges and develop expertise across databases, Kubernetes, cloud infrastructure, and AI technologies. Opportunity to contribute to the evolution of infrastructure supporting AI-powered applications.