Forward Deployed AI Operating Principal

Embed directly with portfolio company teams to bridge the gap between AI platforms and real business workflows — delivering measurable automation outcomes on fast cycle times.

// WHY THIS ROLE EXISTS

Most operating teams advise from a distance. This role is different. Inspired by the forward deployed engineer model — where builders embed directly in the customer’s environment to deliver production outcomes in days, not quarters — this Operating Principal will sit side-by-side with portfolio company operators, hands on keyboard, bridging AI/automation platforms to their specific use cases. You will own the full cycle: discover the problem → architect the solution → build the workflow → prove ROI. Think startup energy meets PE operating discipline. If you’ve spent your career translating complex platforms into customer-specific value at speed, this is the role.

You will operate as a hands-on solution architect and embedded execution leader across Sumeru’s B2B enterprise technology portfolio — part “forward deployed engineer”, part transformation principal. Your work product isn’t a slide deck; it’s running automation in production.

01 — EMBED & DISCOVER
Forward Deploy Into Portfolio Companies
Embed directly with portco CEOs, COOs, CRO’s, CMO’s, and operational teams on-site. Run rapid discovery sprints — time-in-motion studies, ticket and call analysis, process mining, data quality assessments — to baseline current workflows and surface the highest-value automation targets within your first weeks. Approach each engagement the way an FDSE approaches a customer: you don’t just advise, you build.

02 — ARCHITECT & BUILD
Bridge Platforms to Use Cases at Speed
Design and hands-on implement AI/automation workflows that connect the right tools (Claude Code/Cowork, n8n, UiPath, xClaw, custom LLM pipelines) to specific business processes — Tier 1 support deflection, order-to-cash automation, customer onboarding copilots, document intelligence for contracts and invoices. Own the full stack from data integration to deployed workflow. Target: working pilot within 4–8 weeks per engagement, not 6-month roadmaps.

03 — PILOT, MEASURE & SCALE
Prove Value Then Industrialize
Run structured A/B pilots with hard KPIs. Move material volumes of work from human to machine — push automation coverage from <20% to >60% of target transaction volumes without degrading CX. Once a use case proves out, package it for scale across regions, product lines, and additional portcos. Build the playbook as you go: patterns, reference architectures, code, and KPI frameworks that compound across the portfolio.

04 — PORTFOLIO AI ROADMAP
Maintain the Automation Backlog Across Companies
Own a portfolio-wide AI/automation roadmap. Rank initiatives by savings, revenue lift, payback period, and implementation complexity. Collaborate with other operating team members to maintain a shared catalog of proven use cases (support automation, knowledge management + LLM search, professional services copilots, finance ops) — so what works at one company can be rapidly deployed at the next.

05 — DEAL DILIGENCE
Pre-Deal Automation & AI Readiness Assessment
Partner with deal teams on operational diligence. Quantify the AI/automation value creation case — e.g., “$3–5M annual EBITDA uplift via 40% automation of Tier 1 support within 18 months” — with defensible assumptions. Identify critical leadership or org changes needed post-close to execute.

06 — TOOLING & VENDOR STRATEGY
Define Reference Architectures & Build the Vendor Ecosystem
Establish standard reference architectures for the portfolio’s automation stack. Build and manage a preferred vendor ecosystem (implementation partners, AI platform providers, consultants) and negotiate portfolio-wide commercial terms. Stay current on the evolving tool landscape so you’re always deploying the sharpest available solution.

07 — CAPABILITY BUILDING & CHANGE MANAGEMENT
Leave Teams Stronger Than You Found Them
Design org and talent changes with portco leadership — automation product owners, citizen developers, prompt engineers. Stand up AI/automation COEs in key companies. Deliver executive and manager-level training on AI concepts, business case creation, and data-driven decision-making. Coach functional leaders to own and extend programs after your initial deployment so they don’t depend on you permanently.

08 — OUTCOMES STORYTELLING
Translate Results Into Valuation Impact
Build standard ROI tracking frameworks. Package AI/automation achievements into board materials, portfolio reviews, and exit narratives — “automated X% of tickets,” “cut MTTR by Y%,” “improved gross margin by Z pts” — directly supporting valuation re-rating.

This Is Not a Traditional Consulting or Advisory Role
You won’t hand off a playbook and move on. Like a forward deployed engineer, you embed with the team, use their data, build on their stack, and stay until the solution is running in production and delivering measurable results. The rapid cycle between creating solutions and seeing them in action is the point. You will have the autonomy of a startup operator, with the portfolio-wide leverage of a PE platform.

You’re a builder who has spent meaningful time in the field with customers — translating platform capabilities into specific, deployed solutions. You’ve done this at the intersection of enterprise technology delivery, AI/automation tooling, and business process redesign. You move fast, you ship working systems, and you can influence a C-suite while also writing a workflow config.

■ Hands-On Delivery DNA

10–15 years of experience, with 5+ years owning large-scale enterprise technology delivery, operations, professional services, or solution consulting — and a meaningful portion of that time spent in the field, embedded with customers or portfolio companies, not just managing from HQ. You’ve personally built, configured, or deployed production systems, not just directed others to do so. 

■ Transformation Track Record

• Led 2–3+ end-to-end enterprise-scale transformations where you redesigned processes, technology, and org structure — not just tools implementations 
• Can point to quantitative impact: reduction in volume/handle time/cycle time, gross margin or EBITDA improvement, or measurable customer satisfaction gains 
• Demonstrated fast cycle time — you’re known for delivering working solutions in weeks, not quarters

■ AI & Automation Fluency (Practitioner, Not Theorist)
Hands-on experience using workflow automation and agentic tools (Claude Code/Cowork, xClaw, n8n, UiPath, ServiceNow, Zapier, or similar) to orchestrate cross-system flows — you’ve configured these yourself, not just evaluated vendor demos

• Deployed LLM-powered solutions in production: support bots, knowledge base search/summarization, implementation or sales copilots, document understanding (contracts, invoices, tickets)
• Working understanding of RAG architectures, prompt engineering, data/logging/telemetry requirements for AI systems, and the practical limits of current models

■ Platform-to-Use-Case Translation

You have a solution consultant’s instinct for quickly understanding a customer’s environment, mapping their pain points to platform capabilities, and configuring or building the bridge between the two. You thrive in the ambiguity between “here’s a powerful tool” and “here’s exactly how it solves your specific problem.”
■ Functional Breadth
Significant leadership or delivery experience in at least two of: CX/Support, RevOps, FP&A, DevOps/SRE, Professional Services/Implementation, Field Service, or Back-Office Operations. Comfortable collaborating with and influencing leaders across all of these.
■ Executive Influence + Frontline Credibility
Proven ability to operate at Board and C-suite level while also gaining the trust of frontline operators and technical teams by demonstrating you can do the work, not just talk about it. Experience leading orgs of several hundred people and managing meaningful budgets, plus strong change management capability.
■ Financial & Strategic Acumen
Can build robust business cases, interpret P&L and unit economics, and translate automation outcomes into valuation impact. Familiar with SaaS metrics (ARR, NRR, CAC, LTV, gross margin, support cost per ticket, implementation margin).
■ Education
BS in Computer Science, Engineering, Information Systems, or related field (or equivalent hands-on experience that speaks for itself). MBA or advanced degree a plus but not required