Senior Solution Architect – Agentic Sales Technology

Salesforce • Indianapolis, IN

7d • Onsite

About The Position

Salesforce is seeking a Senior Solution Architect to operate at the intersection of deep business process intuition and hands-on agentic engineering. This is a senior individual contributor role at a director level, focused on building, shipping, and influencing at scale. The core mandate is the incremental transformation of prospect management, from lead generation and qualification through lead management and opportunity progression. This role involves layering agentic tooling into existing human-scale workflows, measuring impact, and iterating. The architect will bring coherence to a multi-technology landscape where multiple agentic tools are being implemented, ensuring a cohesive, observable, and scalable system. This role collaborates across Sales Technology, Digital Marketing, and Data Solutions, with accountability for E2E outcomes and direct responsibility for CRM architectural and technology components. The role operates in two modes: 'fast-twitch' for supporting small squads and rapid prototyping, and 'slow-burn' for providing architectural points of view on complex enterprise systems. Speed of learning and shipping are critical, as is practicing and modeling an AI-native development lifecycle (AIDLC) using tools like Cursor, Claude Code, and Codex.

Requirements

• 10+ years of software engineering experience, with 5+ years building Salesforce solutions at scale.
• Builder-architect mindset — you design and build in the same iteration; you are comfortable owning both the POV and the proof.
• Proven ability to assess and simplify business processes before applying technology — process-first is non-negotiable.
• Demonstrated learning agility — a track record of coming up to speed rapidly on both unfamiliar business domains and new technical paradigms; the ability to go from zero context to credible POV in days, not weeks.
• Strong proficiency in agentic architecture: multi-agent orchestration, context engineering, LLM integration, agent lifecycle management.
• Hands-on experience with AI-native development tooling (Cursor, Claude Code, Codex) — practiced, not theoretical.
• Strategic thinker who executes — comfortable holding a long-term POV while delivering working software in days.
• Comfort operating across initiative scales — from focused two-week sprints to multi-quarter enterprise programs.
• Ability to codify patterns into tooling, not just documentation.
• Strong proficiency in Salesforce platform: Sales Cloud, Agentforce, Data Cloud.
• Expertise in software architecture patterns: microservices, event-driven, distributed systems.
• Exceptional communication — equally fluent with engineers and business partners.
• High agency, low ego — failure is data, not identity.

Nice To Haves

• Broad Prospect-to-Cash domain knowledge preferred— pattern recognition across lead management, opportunity management, quoting, ordering, and renewals; you don't need to be a deep expert in every area, but you need to plug in quickly and credibly.
• Experience building large-scale Salesforce implementations spanning multiple clouds and integration layers — including data modeling, security model design, and cross-org architecture.
• MuleSoft — experience designing integration architectures for P2C data flows (lead handoffs, order sync, entitlement propagation) using API-led connectivity and event-driven patterns.
• Agentforce platform depth — hands-on experience building custom agents using Agent Builder, defining agent topics and actions, grounding agents with Data Cloud Retrieval Augmented Generation (RAG), and deploying agents across Sales Cloud surfaces (Einstein Copilot, Service Cloud, Slack).
• Data Cloud — experience modeling unified customer profiles (Individual, Contact Point, Engagement), building calculated insights and segmentation for agent grounding, and configuring Data Cloud activations that feed downstream agent actions.
• Knowledge of best practices for MCP (Model Context Protocol) and A2A (Agent-to-Agent) development for agent interoperability — including how to expose Salesforce data and actions as MCP tools consumable by external orchestrators.
• Experience instrumenting agent observability: structured logging of agent reasoning traces, tool call latency, hallucination detection, and feedback loop design for continuous prompt iteration.
• Experience with buyer engagement or conversational intelligence platforms (e.g., Qualified, Gong, Chorus) and how they integrate with Salesforce as data sources for agent context.
• Public cloud experience (AWS preferred) — particularly Lambda, API Gateway, Bedrock, or S3 in the context of hosting agent tools, context stores, or retrieval backends that extend Salesforce agents.

Responsibilities

• Own the architectural foundation for agentic selling across the full marketing-to-sales pipeline — lead generation, lead qualification, lead management, and opportunity progression.
• Drive an incremental transformation approach: layer agentic tooling into live systems deliberately, instrument for impact, and create tight feedback loops for innovation and iteration.
• Define and track meaningful architectural success metrics — including agent-driven pipeline conversion lift, lead qualification rates, routing accuracy, and agent deflection/escalation patterns.
• Assess and rationalize the current multi-technology landscape, identifying conflicts, dependencies, and integration gaps.
• Design multi-agent orchestration patterns that are coherent, observable, and production-grade — not prototypes layered on existing systems.
• Ensure lead routing, territory assignment, and CRM workflows are refactored — incrementally — to support agent-scale throughput and decision-making.
• Lead context engineering practices — designing the information architecture, retrieval strategies, and grounding mechanisms that give agents the right context to act reliably and accurately.
• Define agent lifecycle management practices: design, testing, iteration, observability, and governance from prototype through production.
• Assess and simplify business processes before applying technology; show the business a working prototype within days on fast-twitch engagements.
• Develop and Apply agentic architecture patterns that apply across the the full Prospect-to-Cash domain.
• Define agent lifecycle practices: design, testing, iteration, observability, and governance from prototype through production.
• Codify architecture patterns and best practices directly into tooling so they travel with the work, not sit in a document.
• Serve as an early architectural point of view across multiple initiatives, technology, product innovations.
• Serve as a trusted advisor to the Product, Engineering organization as well as Senior Leadership to navigate through ambiguity.
• Engage across initiatives of all scales, bringing the right level of architectural rigor to each — from rapid prototyping engagements to complex, multi-system programs.
• Break down intake requests for complex enterprise programs to achieve incremental outcomes, minimize dependencies, and reduce downstream cost and risk.
• Identify and escalate process or data blockers that will slow agentic transformation; make them visible rather than working around them.
• Act as connective tissue between fast-moving squads and the domain teams that own core business technology — ensuring fast-moving work lands on solid architectural ground.
• Approach every engagement with a strategic lens — understand the business outcome before proposing a technical direction.
• Validate with the business first, use AI to generate evidence of value quickly, then scale.
• Accelerate business analysis and decision-making by applying AI — reducing the time between a question and a validated answer from weeks to days.
• Work with one empowered business decision-maker per engagement, not a committee.
• Influence enterprise architecture direction on complex programs where the fast-twitch model isn't appropriate, without losing the bias for action.
• Practice AIDLC daily — Cursor, Claude Code, and Codex are your default toolchain, not optional accelerators.
• Help establish AIDLC standards across the broader engineering team: AI-assisted architecture review, AI-generated documentation, AI-augmented testing, agent-assisted delivery workflows.
• Codify best practices into shared context files, spec markdowns, and harness configuration — source-controlled and accessible to the whole team.
• Make your work discoverable and reusable by default; contribute to knowledge sharing across squads continuously.
• Sit close enough to users and business partners to feel what they feel — user empathy is a prerequisite, not a nice-to-have.
• Assess and challenge underlying business processes before agentifying them — simplification is often more valuable than automation.
• Participate in outcome-based accountability cadences — be prepared to answer: what did the business get from their investment this week?
• Treat failure as data: take intentional action, learn fast, and adjust.
• Provide architectural POVs on complex, highly integrated programs where fast-twitch pace isn't appropriate.
• Apply AI to accelerate business analysis and decision-making even when the delivery model is more traditional.
• Ensure agentic components being built on fast-twitch engagements are architecturally compatible with core enterprise systems they'll eventually integrate with.
• Influence data architecture and integration strategy across Salesforce Sales Cloud, Agentforce, Data Cloud, and Snowflake.

Benefits

• time off programs
• medical
• dental
• vision
• mental health support
• paid parental leave
• life and disability insurance
• 401(k)
• employee stock purchasing program

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed