> Markdown version of [/jobs/ext/4566694-sr-staff-software-engineer-agentic-applications](https://www.wearedevelopers.com/jobs/ext/4566694-sr-staff-software-engineer-agentic-applications). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Staff Software Engineer, Agentic Applications - **Company:** Databricks - **Location:** Mountain View, CA, United States - **Experience:** Expert - **Salary:** $233,900.0 - $321,650.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Audit Trail, Software as a Service, Cloud Computing, Continuous Integration, Software Design Patterns, Python (Programming Language), Netsuite, Regression Testing, OpenAI, Salesforce.Com, Scala (Programming Language), Service Design, Software Engineering, Systems Architecture, TypeScript, Web Application Frameworks, ReactJS, Retrieval-Augmented Generation, DSPy (Programming Framework), Large Language Models, Multi-Agent Systems, Backend, Agentic-AI, Data Management, Machine Learning Operations, Virtual Agents, Human in the Loop, Databricks, Zuora - **Published:** November 4, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=ebf6265d4a111dcc ## About the Role * 12+ years of software engineering experience, with a clear track record of technical leadership and ownership of large-scale production systems. * 3+ years of hands-on experience building and running LLM-powered or agentic applications in production, such as agents, RAG pipelines, tool use, multi-agent orchestration (LangGraph, OpenAI Agents SDK, DSPy or equivalent), and evaluation frameworks for generative outputs. * Strong backend and distributed-systems skills: API and service design, data modeling, idempotency, consistency and transactional integrity, and observability. * Proven experience designing human-in-the-loop systems: approval workflows, review interfaces, audit trails, and guardrails for probabilistic systems operating in regulated or high-stakes domains. * Experience leading technical architecture for complex business domains, ideally Quote-to-Cash, Order-to-Revenue, billing, pricing or other revenue-critical enterprise systems. * Full-stack fluency. You are comfortable across modern web frameworks (React/TypeScript), backend services (Python, Java, Scala or Go), and data platforms. * Ability to work directly with non-engineering stakeholders and turn ambiguous business workflows into clear agent and system architecture. * Experience mentoring engineers and improving technical execution across a team, without relying on positional authority., * Domain experience with CPQ, deal management, subscription management, or revenue recognition in a high-growth SaaS or consumption-based business. * Experience building first-party applications on top of, or as replacements for, enterprise SaaS platforms (e.g., Salesforce CPQ/Revenue Cloud, NetSuite, Zuora). Platform-specific configuration expertise is a plus but not a requirement. * Experience with the Databricks platform (Databricks Apps, Lakebase, Unity Catalog, Mosaic AI Agent Framework, MLflow tracing and evaluation). * Familiarity with SOX controls, audit requirements, and financial-systems compliance. Pay Range Transparency ## Description * Own the agentic architecture for Quote-to-Cash. Define and lead the target technical architecture across Configure-Price-Quote, deal management, ordering, subscriptions, investments, billing and revenue recognition. Drive the move from vendor-configured workflows to first-party, AI-native applications built on Databricks Apps, Lakebase, Unity Catalog and Mosaic AI. * Build production agents that sellers and Deal Desk trust. Design and ship LLM-powered agents for deal guidance, quote generation and summarization, multi-option proposals, approval insights, and pricing and discount recommendations. Work directly with sales, Deal Desk and Finance to define what "good" looks like for each workflow. * Make agent reliability a core system property. Own the eval and quality system for this domain: per-workflow quality criteria, offline eval suites, LLM-as-judge and human-labeled benchmarks, behavioral regression testing in CI/CD, online tracing, and feedback loops that improve agents based on what users approve, edit and reject. * Design human-in-the-loop controls for revenue-critical decisions. Build the separation between what an agent proposes and what gets committed, plus diff-based review, approval routing, audit logging, and policy guardrails. Together these decide when an agent can act on its own and when a person has to approve, especially for pricing, discounting, contract terms and order creation. * Treat systems of record as agent tools. Architect tool-calling and MCP-style interfaces that give agents governed, run-time access to pricing, product catalog, order, contract, subscription and billing data. Follow single-source-of-truth principles across CRM, ERP and billing systems. * Protect financial integrity at scale. Make sure agent-assisted quoting and ordering produce accurate, compliance-grade outputs downstream: order validation, billing, invoicing and revenue recognition, where errors directly affect financial reporting. * Lead the migration path. Act as the bridge between today's production quoting platform and the target agentic architecture. Sequence the migration so the business keeps scaling without disruption, and decide what to retire, rebuild or wrap. * Raise the engineering bar. Lead architecture reviews, establish design patterns for agents (orchestration, memory, retrieval, tool use, multi-agent coordination), and mentor senior and mid-level engineers on production-grade agentic engineering. * Earn cross-functional credibility. Partner with Finance, Deal Desk, Sales Ops, Legal and the broader Platform engineering org. Turn complex commercial processes into clear technical roadmaps and measurable business outcomes.