Staff Engineer

About the Role: We are looking for a Staff Engineer to serve as the foundational leader of our Agentic Platform Team. This is a high-impact player-coach role designed for a seasoned expert who can navigate the ambiguity of the evolving AI landscape while maintaining a rigorous focus on production-grade engineering. As a technical North Star, you will bridge the gap between high-level product vision and low-level system execution. You will be responsible for the brain and nervous system of our platform—architecting how AI agents reason, remember, and securely access enterprise data. If you have a decade of experience building distributed systems and are now obsessed with the intricacies of agentic workflows and RAG at scale, this is your next challenge. What You’ll Do Architect Agentic Infrastructure: Lead the design of high-performance Vector Database architectures and long-term agent memory systems to power efficient, high-context AI reasoning. Scale Production RAG: Build a multi-use-case Retrieval-Augmented Generation system. You will define chunking strategies, embedding pipelines, and retrieval ranking logic that ensure accuracy and freshness across thousands of customers. Build the Connectivity Layer: Design an extensible, developer-friendly Enterprise Connectors Platform to ingest real-time data from Slack, Jira, and Workday, ensuring secure multi-tenant isolation and robust error recovery. Modernize Platform Delivery: Lead the transition to GitOps-driven deployments using Argo CD and Kubernetes, ensuring our AI services are as reliable as they are innovative. Define AI Observability: Implement deep-trace visibility and benchmarking frameworks using tools like Langfuse to monitor agent performance, planning, and tool-use interactions. Provide Technical Direction: Act as the final decision-maker for complex design trade-offs, providing mentorship to the team while remaining deeply hands-on in the codebase. What You Have 10 Years of Experience: A proven track record of designing and implementing high-volume distributed systems or consumer-grade AI platforms. AI/LLM Specialization: Deep familiarity with agentic patterns (memory, planning, tool-use) and orchestration frameworks like LangChain or LlamaIndex. Infrastructure Mastery: Expertise in Kubernetes and Argo CD, with the ability to manage sophisticated CI/CD pipelines in a cloud-native environment. Data Engineering Prowess: Deep understanding of Vector Data management (indexing, similarity search) and event-driven ingestion pipelines (Kafka, Kinesis). Security & Multi-tenancy Mindset: Experience building for high-growth SaaS environments where data privacy, rate limiting, and tenant isolation are non-negotiable. Exceptional Coding Skills: Strong proficiency in modern backend languages such as Go, Python, Java, or C. Academic Foundation: A Bachelor’s in Computer Science is required; a Master’s or PhD in CS, Machine Learning, or a related field is highly preferred. Mandatory and Required Skills for All ThoughtSpot Roles Spotters are expected to demonstrate AI literacy and workflow integration to include to ability to: Comfortably and confidently integrate artificial intelligence into their daily workflow to increase productivity and quality. Hands-on experience to leverage AI tools (industry-leading LLMs) to increase productivity, automate routine tasks, and improve work quality. Speak to the experience of using AI for research, content creation, and document summarization while maintaining ownership of judgment and final decisions. Write effective prompts to get the most accurate and creative results from AI tools. Spotters are expected to exemplify these key traits and AI Mindset: Curiosity in exploring new AI tools Adaptability to quickly learn and implement new, emerging AI technologies Critical thinking to know when to identify when AI should be used versus when human judgement is necessary This combination of curiosity, adaptability, and discernment ... (truncated, view full listing at source)