Forward Deployed Engineer (Revenue)

OpenGov•Chicago, IL

4d•$150,000 - $165,000•Hybrid

About The Position

OpenGov is seeking a Forward Deployed Engineer to serve as a dedicated practitioner within the Applied AI team, focused on Sales/GTM use cases across the revenue organization — SDR outbound, AE pipeline and opportunity management, and CSM account management and renewals — along with the account/opportunity intelligence that feeds all three. This role sits at the intersection of seller workflows, revenue systems, and applied AI, and includes hands-on GTM engineering: building the workflows, integrations, and automations across revenue systems that make sellers faster. It exists to systematically redesign how OpenGov's revenue organization runs its motion in an AI-native world, from lead through cash and the renewals that follow. This is a technical role applied in a traditionally non-technical space. You bring a sharp problem-solving mind, comfort with modern AI tools, and the instinct to decompose a complex, manual seller workflow into its components — and then design something fundamentally better. You need the ability to think from first principles, build trust with sellers and RevOps before designing for them, and own the full lifecycle from problem discovery to solution adoption. You will build the agents and systems that turn raw go-to-market signal (procurement data, contract/vendor intelligence, account and contact records, call and email activity) into action sellers actually take — signal-to-sequence automation for SDR outbound, next-best-action and deal-risk surfacing for AE opportunity management, and account-health and renewal-risk surfacing for CSMs. You will also serve as product owner for systems built entirely in house, and be the primary AI capability advisor for the revenue organization. This role operates within the Applied AI team's technical ecosystem. You will partner closely with a dedicated analytics/ops counterpart on the sales side, partner with AI and data engineers on complex builds, and work directly with sales leadership, RevOps, and SDR/AE/CSM teams for domain guidance and go-to-market context. Given the revenue-centric focus of this work, this role requires a genuine interest in driving conversion across the funnel (such as contact-to-meeting rate, S1/S2 conversion, and win rate), along with deal-cycle length and renewal outcomes. Pipeline management itself sits with sales leadership and RevOps, and you will work closely with them. You will not own KPI definitions or reporting, but you will apply the necessary rigor and ROI framing to make sure what gets built is actually moving those numbers. You will have access to data, ownership, and core responsibilities at a level comparable to senior sales leadership, and are expected to exercise that with corresponding discretion.

Requirements

Bachelor's degree in Information Systems, Computer Science, Business Analytics, Data Analytics, or a related field.
4–7 years of experience in technical product management, sales/revenue operations, data science, GTM strategy or operations, solutions architecture, or a closely adjacent function. Backgrounds vary: a technical PM, a data scientist, a sales ops analyst who builds, or a GTM strategist who has taught themselves to build with Claude are all strong fits. Deep software engineering backgrounds are welcome when paired with sales or revenue operations experience. We need someone who has been hands-on, on the ground, building and improving things — not advising from a distance.
Demonstrated experience using AI tools in a professional context — not just familiarity. You have been actively using tools like Claude, Cursor, or Codex, as well as agentic orchestration systems like n8n, make.com, or Airia, to meaningfully improve how you or your team works, and you can speak concretely about what you've built or changed as a result.
Production experience building and operating agents, including tool use, context engineering, and evaluation frameworks — you can point to something you shipped and describe how you knew it was working.
Past experience/exposure to GTM and Sales at a SaaS company.
Exceptional problem-solving and first-principles thinking: you can look at a messy, manual seller process, pull it apart into its components, and design a fundamentally better solution — not just automate what exists today. You approach unfamiliar domains as an asset, not a liability.
Strong user research and discovery skills: you build trust with sellers and RevOps before designing for them, surface the root problem underneath the surface problem, and validate solutions with the people who will actually use them.
Ability to bridge technical and business stakeholders: you can explain a complex AI workflow to a sales stakeholder, articulate data access tradeoffs to a compliance stakeholder, and translate business requirements into engineering specs — all in the same week or even day.
Comfort working in a code-first approach to traditionally no-code enterprise sales systems (Salesforce, Outreach, Gong, or similar). Familiarity with how enterprise revenue data flows across systems is a plus.
Strong analytical skills: comfortable with data in spreadsheets, dashboards, or basic BI tools (Tableau, Looker, or similar), and working fluency with SQL; able to extract insight from data and frame it clearly for both technical and non-technical audiences.
Self-starter with high initiative: you see opportunities, propose solutions, and drive them forward without waiting to be told. This role will involve significant ambiguity, and the right person will find that energizing rather than paralyzing.
Exceptional written and verbal communication: you write clearly, you do not create information silos, and you know how to tailor your message to the audience in front of you.

Nice To Haves

Prior experience in sales/revenue operations, customer success operations, or a forward-deployed/solutions-architecture role.
Experience with Claude Code, the Claude Agent SDK, or building MCP servers.
Familiarity with AI governance concepts: prompt design, data access controls, audit trails, and responsible AI principles in sensitive-data environments.
Experience building or deploying workflow automations or internal tools without engineering support.
Familiarity with Snowflake, Looker, or similar data platforms at a data analysis level (not data engineering).
Applied ML and experimentation background: A/B testing, propensity models, recommendations, or lead/account scoring.
Experience working in a fast-moving, high-growth SaaS environment where the org is actively evolving and priorities shift quickly.
Experience working at the intersection of Sales and Marketing, or Sales and RevOps — roles that required navigating cross-functional data ownership and system permissions.
Prior exposure to public sector or B2G sales, or other environments with elevated data handling standards.

Responsibilities

Own the AI-native signal-to-action layer for Sales: design, build, and continuously improve the agents and automations that turn account/contact/product signal into qualified SDR sequences, AE opportunity actions, and CSM account and renewal actions, with clear human review and approval points before anything reaches a prospect or customer.
Build for AE and CSM workflows specifically, not just top-of-funnel outbound: deal-risk and next-best-action surfacing for AEs managing opportunities through the pipeline, and account-health, renewal-risk, and expansion-signal surfacing for CSMs managing customers through renewal — this role's scope is the revenue organization, not one segment of it.
Serve as product owner for systems built entirely in house: drive the roadmap, prioritization, and quality bar for the call- and deal-intelligence and other revenue systems the Applied AI team builds, in partnership with sales leadership and the RevOps/analytics counterpart.
Design the human oversight for every agent you ship: approval gates, escalation paths, and clear ownership boundaries so SDRs, AEs, and CSMs stay in control of what goes out under their name — an agent recommends and drafts, a human approves and sends.
Build and run evaluation frameworks for agent behavior: seed scenarios, scoring rubrics, and regression checks that prove an agent is producing good contacts, good sequencing, good deal guidance, and good call and deal intelligence output before and after it goes into production — not just that it runs.
Instrument and monitor production agent activity: track model and tool calls, tie agent actions back to pipeline and revenue outcomes, and give sales leadership and RevOps a clear read on what the agents are actually doing and whether it's working.
Map and redesign seller workflows with an AI-native lens: go deep with SDRs, AEs, CSMs, and RevOps to understand current-state processes — signal sourcing, contact qualification, sequencing, account prioritization, deal support, renewal management — identify where AI creates leverage versus risk, and translate findings into prioritized action plans.
Do the GTM engineering that connects revenue systems: build against Salesforce, Outreach, Gong/call data, and third-party signal sources (e.g., procurement and contract data), and surface shared infrastructure opportunities to prevent duplicated builds with other Applied AI efforts. This role is meant to create economies of scale, not n+1 point solutions.
Partner with AI platform, systems, and/or data engineers when required: clearly define requirements, translate business needs into actionable technical specs, and own outcomes on the business side — build first, then delegate backend complexity (data modeling, integrations, infrastructure) to platform engineering.
Serve as the primary AI capability advisor for Sales: help sales and RevOps leaders understand what is possible with AI today, evaluate build vs. buy tradeoffs, identify where AI should not be applied due to compliance or brand-risk constraints, and set realistic expectations on scope and timeline.
Drive adoption and change management for tools you build: shipping is not the finish line. You own ensuring that agents and workflows are understood, trusted, and refined based on real feedback from the SDRs, AEs, and CSMs who depend on them.
Maintain a living roadmap of AI initiatives across Sales: track ideas, in-flight projects, and delivered solutions; report on impact (e.g., pipeline contribution, conversion lift, call- and deal-intelligence adoption); and continuously reprioritize based on business need and resource availability.
Keep your impact visible to executive leadership: visibility is a core part of this role. Report on business impact on a regular cadence (monthly, in terms the executives you support can defend — conversion, pipeline contribution, renewal outcomes), alongside weekly check-ins with Applied AI leadership.
Communicate progress, priorities, and blockers clearly across stakeholders: written updates, structured check-ins with sales leadership, RevOps, and Applied AI leadership, and crisp escalation when decisions require broader alignment.