Senior Manager, AI Solutions Architect

The Monetization team is building AI agents into their day-to-day workflows and extending that capability across the broader organization to enable sellers on pricing and packaging. You will be the internal steward of this agent workflow system, maintaining it, extending it, and working with the team to find where AI-assisted workflows create the most value. This role sits at the intersection of engineering and analysis – requiring the business lens of a product manager and the technical depth to design, build, and evolve AI agent workflows. The ideal candidate thinks in systems, understands how teams operate, and can move fluidly between strategy and execution. What you’ll do: Define the technical architecture for our agent portfolio (integration patterns, platform choices, guardrails) Design and govern multi‑agent orchestration patterns – how agents hand off tasks, share context, and operate within composite AI systems Own the “connective tissue” layer: how agents, APIs, workflows, and enterprise platforms (Agentforce, Slack, Snowflake, Claude) are wired together and scaled reliably Set standards for how agents are built, tested, and evaluated Own agent performance post-deployment – monitoring, iteration, quality Make platform calls (Agentforce vs. custom, Slack-native vs. standalone, build vs. buy) Own agent identity and session design – author and maintain agent context files (identity files, session startup protocols, memory structures, and skill definitions) that govern how agents behave consistently across sessions, users, and evolving workflows Manage system health and reliability – monitor, log, and handle errors; keep the agent infrastructure running as team dependency grows Own the deployment lifecycle – manage configuration changes, deploy updates, and debug issues in the production environment without requiring outside engineering support Experience: Built or deployed LLM‑based agentic workflows (not just chatbots) Hands‑on experience with agent orchestration frameworks (e.g., LangChain or equivalent) and multi‑agent coordination patterns Treats prompt engineering as foundational but not sufficient – one component within a broader orchestration architecture Thinks in systems, not demos – cares about maintainability, evaluation, and scale Translates fluently between strategy/business and engineering Working Python proficiency and Linux comfort – can read the codebase, make configuration changes, and operate the infrastructure stack independently LLM context file design as a craft – knows how to write durable agent context that holds up across sessions Self‑sufficiency – able to deploy, debug, and iterate without waiting for outside engineering help; owns the full loop from design to production Domain fluency in monetization workflows – quick understanding of deals, contracts, discounting, and revenue Equal Opportunity Employer Salesforce is an equal opportunity employer and maintains a policy of non‑discrimination with all employees and applicants for employment. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. Compensation In the United States, compensation offered will be determined by factors such as location, job level, job‑related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. The typical base salary range for this position is $172,500 – $260,100 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range is $207,800 – $285,800 annually. #J-18808-Ljbffr salesforce.com, inc.