Software Architect, AI Applications

Software Architect, AI Applications

As an Architect in Gong's AI Applications group, you will lead the architecture and technical direction of the Revenue Harness , the agentic framework that powers an AI teammate for every revenue professional. The assistant understands a user's deals, customers, and pipeline. It can reason, plan, and act on the user's behalf, grounded in the richest revenue data in the world.

This is a hands-on role with broad influence. You will embed with teams where needed, guide design decisions, and help build scalable, extensible agentic systems on state-of-the-art AI technology.

• Agentic Core: Agent orchestration, planning and reasoning loops, multi-step execution, and multi-agent coordination for long-running revenue workflows.
• Tools & Integrations: A secure, extensible tool ecosystem, including MCP-based tools and connectors, that lets agents read from and act on Gong data, CRMs, email, and calendars.
• Context & Memory: Retrieval pipelines over calls, emails, deals, and accounts, using RAG, hybrid search, and knowledge graphs, plus short- and long-term memory.
• Quality & Trust: Offline and online evaluation of accuracy, groundedness, and task success, along with guardrails, permissions, and human-in-the-loop controls.
• Model Platform: Multi-provider model routing, streaming, LLM observability, and cost and latency management.
• Runtime & Execution: High-concurrency infrastructure for stateful and stateless agents, including lifecycle management, isolated workers, task queues, and distributed state persistence.
• Deployment & CI/CD: Cloud-native Kubernetes deployments with multi-region rollout, zero-downtime updates, canary releases for non-deterministic workflows, and strict tenant sandboxing.
• Revenue Harness Architecture: Serve as the group's central technical leader. Define the reference architecture and the contracts between agents, tools, context, and models, so that teams build on a shared, scalable foundation.
• Hands-On Engineering: Embed in teams across domains and contribute directly to design and code.
• Quality & Reliability Standards: Set the group's eval methodology, regression gates, and production monitoring for non-deterministic systems.
• Mentorship & Engineering Standards: Raise the bar on design quality and best practices across the group.
• AI-Assisted Development: Drive adoption of coding agents, AI code review, and agentic workflows to improve velocity and quality.
• Reliability on Non-Deterministic Foundations: Make LLM behavior trustworthy through structured outputs, tool-call validation, fallbacks, and well-defined failure modes.
• Context at Enterprise Scale: Get the right information into the context window efficiently when the data spans millions of conversations, deals, and accounts.
• Secure, Permission-Aware Agents: Enforce data boundaries, user permissions, and tenant isolation, and defend against prompt injection and unintended actions.
• Quality, Cost & Latency Trade-offs: Balance them through model selection, caching, and routing.
• Ambiguity & Divergence Across Teams: Bring clarity to loosely defined cross-team problems, surface blind spots, and steer teams toward aligned, reusable patterns.
• Speed vs. Long-Term Evolution: Catch architectural gaps early and make pragmatic trade-offs, so that systems keep pace with rapidly changing models and tooling.
• The Future of Revenue Work: Help sales professionals shift their time from busywork to selling.
• A Higher Engineering Bar: Build stronger, more autonomous teams through better design practices and mentorship.
• Consistent, Fast Delivery: Reduce duplication and accelerate execution without compromising long-term platform health.
• 10+ years of backend development, with strong expertise in Java and modern frameworks such as Spring Boot.
• Proven Staff or Principal-level impact across multiple teams, with the ability to lead through influence rather than authority.
• Hands-on experience shipping production LLM applications, such as agents, tool calling, RAG, or structured generation, and an understanding of their failure modes.
• Deep expertise in designing large-scale distributed systems, including asynchronous, event-driven, and streaming architectures.
• Versatility in diving into unfamiliar codebases, switching context across teams, and spotting high-impact opportunities.
• A passion for mentoring, and the ability to communicate complex ideas clearly to diverse stakeholders.
• Genuine curiosity about the AI landscape, and a track record of driving adoption of AI-assisted development.
• Experience with agent frameworks and protocols (e.g., MCP, Spring AI, LangChain/LangGraph) or with building an in-house equivalent.
• Experience with LLM evaluation (LLM-as-judge, golden datasets, online monitoring), observability, and multi-provider routing.
• Experience with vector databases, search infrastructure (e.g., Elasticsearch/OpenSearch), or knowledge graphs.
• Familiarity with AI security concerns, including prompt injection, data leakage, and agent permission models.
• Working knowledge of Python alongside a JVM stack.
• Background in B2B SaaS, CRM, or revenue technology.

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