Senior Solution Architect – Agentic Sales Technology

Salesforce • Indianapolis, IN

9d

About The Position

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.
Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.
We're looking for a Senior Solution Architect who operates at the intersection of deep business process intuition and hands-on agentic engineering. This is a senior individual contributor role operating at director level — you will be a hands-on technical leader who builds, ships, and influences at scale without relying on organizational authority. You will not have direct reports, but your impact will be felt across an architecture organization. You think strategically and execute rapidly, often in the same week.
The core mandate of this role is incremental transformation of prospect management — from lead generation and qualification through lead management and opportunity progression. This pipeline was architected for human-scale workflows, and the business expectation is clear: rapidly layer in new agentic tooling (Salesforce on Salesforce), measure impact, innovate, and iterate. You won't be rebuilding from scratch — you'll be threading intelligent agents into a live, complex system while keeping it moving. That requires as much judgment as it does technical skill. Wherever there is an opportunity to apply agentic AI to transform a business process, your first question on every engagement is always: does this process make sense before we agentify it?
You'll bring coherence to a multi-technology landscape where multiple agentic tools are being implemented simultaneously — ensuring that buyer engagement agents, conversational sales agents, and the underlying CRM and routing infrastructure form a coherent, observable, and scalable whole.
This role sits at the intersection of three organizational pillars — Sales Technology, Digital Marketing, and Data Solutions — and operates as a key architectural partner across all three. You will be accountable for E2E outcomes while holding direct responsibility for the architectural and technology components that live in CRM. Strong cross-pillar collaboration is not optional; it's how this role delivers.
This role oscillates between two modes. In fast-twitch mode, you're supporting small, focused squads — simplifying the process, and getting a working prototype in the hands of users within days, making key architectural decisions that set the squad up for delivering a successful business outcome. You're not waiting for alignment; you're generating it through working software. In slow-burn mode, you're providing early architectural points of view on complex enterprise systems that don't fit the fast-twitch pace model — influencing the direction of large, integrated programs where the cost of getting the architecture wrong compounds over time. Strategic thinking and rapid execution are both required; this role demands you bring both, calibrated to the situation.
You'll work across initiatives of varying scale and complexity — from focused two-week sprints to multi-quarter enterprise programs — and your job is to bring the right architectural posture to each one. Speed of learning and speed of shipping are equally important here — rapid learning is what makes rapid delivery sustainable.
Equally important: this role advances how we build. You'll practice and model an AI-native development lifecycle (AIDLC) — using Cursor, Claude Code, and Codex not as productivity shortcuts but as a fundamentally different way of working. Architecture patterns, best practices, and compliance checks should be baked into the tooling, not left to individual teams to figure out.

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
• 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
• 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

• 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
• Design context engineering strategies — information architecture, grounding, memory patterns, and retrieval design — that give agents the right context to act reliably
• 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
• For complex enterprise programs, break down intake requests to achieve incremental outcomes, minimize dependencies, and reduce downstream cost and risk — this is where judgment about what not to build matters as much as what to build
• 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