Senior Solution Builder, Salesforce

• 4–8+ years of software engineering, AI delivery, or technical consulting, with hands-on ownership of production systems (Builder: 4–6 years; Senior Builder: 6–8+ years)
• Proven track record building and shipping production software — not prototypes, not demos, but systems that run in the real world
• Hands-on LLM integration experience: agent frameworks (LangChain, LlamaIndex, or equivalent), prompt engineering, and responsible AI practices in production
• Fluency in Python, JavaScript/TypeScript, Java, or Apex — and the range to add to that list as the work demands
• Deep experience in data modeling, APIs, and integration patterns — able to design them, not just consume them
• Ability to work autonomously in ambiguous, fast-moving customer environments with real delivery accountability
• Strong enough communication skills to engage technical stakeholders and explain architecture decisions clearly
• Ability to operate effectively as the sole builder in a small, high-trust pod
• Willingness to travel ~25% of the time, working directly at customer sites

Desirable

• Salesforce is hiring Solution Builders — senior individual contributors who are the technical engine of a 2-person delivery pod
• You partner with a Director who owns strategy and customer relationships; you own the build
• You design and deploy production-grade agentic AI systems directly at customer sites, working hands-on every day
• No overhead, no managing other builders — just you, your pod partner, and the work in front of you
• This is a hands-on IC delivery role
• Your output is working software in production at the customer
• Architect, build, and deploy production-grade Agentforce and agentic AI solutions end-to-end — this is your core job
• Design and implement AI agents, multi-agent orchestration, agentic workflows, and the integration patterns that hold up in enterprise environments
• Own data pipelines, system integrations, and production AI infrastructure across Salesforce, Snowflake, Databricks, and customer platforms
• Build rapidly from ambiguous requirements — prototype to validate, then engineer to last
• Resolve complex technical blockers: data integration failures, orchestration breakdowns, model deployment issues
• Drive solutions from first commit through production handoff and active customer consumption
• Operate as the technical execution half of a 2-person pod — the Director shapes what gets built and why; you own how it gets built and make sure it ships
• Translate strategy and customer context from the Director into concrete technical decisions and daily delivery progress
• Communicate blockers, risks, and architecture tradeoffs clearly and early so the pod stays in sync
• Represent technical depth in customer-facing sessions — architecture reviews, demos, working sessions, production readouts
• Flag patterns, gaps, and opportunities you see in the field back to the Director and to Agentforce product teams
• Embed directly with customer engineering and data teams — not remotely, not occasionally, but as a consistent presence
• Build trust with customer technical counterparts through delivery quality and technical credibility, not just relationship management
• Guide customer teams through the technical decisions required to operationalize AI at scale
• Surface expansion signals and customer feedback to the Director
• Contribute reusable patterns, reference implementations, and field insights back to the broader Builder org
• Stay sharp on Agentforce platform evolution, agentic delivery patterns, LLM advances, and the competitive AI landscape
• Codify what you learn — reusable components, playbooks, and lessons from the field that raise the bar for the whole org
• Bring a growth mindset to hard problems: when something breaks or doesn't work, you dig in rather than escalate