Buzz Solutions - Senior Forward Deployed Engineer
Requirements
• 6+ years in solutions engineering, forward deployed engineering, implementation, or professional services at a technical product company — hands on the keyboard, owning customer outcomes. • 6+ years • Pre-sales experience. You've partnered with account executives through a sales cycle: running technical discovery, scoping implementations before signature, and being the person the customer's engineers trust in the room. • Strong Python or TypeScript, and genuine fluency integrating third-party systems via APIs, webhooks, and data pipelines. • Strong Python or TypeScript • Hands-on experience building with LLMs and agentic systems — prompting, tool use, retrieval, orchestration, and evaluation. You've put something agentic in front of real users, not just prototyped. • Hands-on experience building with LLMs and agentic systems • Enterprise credibility. You've navigated security review and architecture scrutiny, and can hold your own with skeptical engineers on the other side of the table. • Enterprise credibility. • An eye for the next opportunity. You notice when a customer problem is something we could solve, and you can scope it well enough for Sales to act on. • An eye for the next opportunity. • Genuine comfort with ambiguity. You can walk into an undefined problem, pick a direction, and ship. • Genuine comfort with ambiguity. • Excellent communication. You can explain a technical tradeoff to a utility operations director and a customer constraint to an engineer, and be understood by both. • Excellent communication. • Willingness to travel roughly 40% of the time for customer sites and team onsites. • Willingness to travel • Experience in utilities, energy, or another regulated industry with long enterprise sales cycles • Familiarity with computer vision, geospatial data, or large-scale imagery pipelines • Experience as the first or earliest technical hire in forward deployed engineering, customer engineering, or implementation — building a function rather than joining one • Background in RevOps or internal tooling that earned real, sustained adoption
Responsibilities
• Pre-sales partnership • Ride alongside Sales as the technical partner. Join discovery calls, understand the customer's inspection workflow and data reality, and translate it into what a Buzz deployment looks like for them. • Ride alongside Sales as the technical partner. • Scope and size implementations before anything is signed — integration effort, data requirements, timeline, and what's genuinely in and out of reach. • Scope and size implementations • Run pilots and proofs of value that prove the platform against the customer's own imagery and assets, not a canned demo. • Run pilots and proofs of value • Clear technical objections. Handle architecture questions, security review, and the data-handling scrutiny that comes with regulated utility buyers. • Clear technical objections. • Deployment and Implementation • Own deployments end to end — configuration, integration, validation, and handoff — for customers with real operational constraints. • Own deployments end to end • Build the integrations that connect PowerAI to customer systems: asset management, GIS, inspection data pipelines, and reporting tools. • Build the integrations • Use AI workflows to compress delivery. Turn what would be bespoke engineering work into repeatable, agent-assisted implementation, so delivery speed isn't capped by headcount. • Use AI workflows to compress delivery. • Find and scope expansion. You'll be closer to the customer's operation than anyone else. Spot the next use case, size what it takes, and hand Sales a qualified, technically validated opportunity. • Find and scope expansion. • Bridge field and core engineering. Solve what you can in the field, identify recurring patterns that belong in the product, and bring back clear, reproducible requirements rather than one-off escalations. • Bridge field and core engineering. • Product and Internal Enablement (~20%) • Build and ship reusable deployment tooling and AI agents across customer delivery and GTM workflows — and drive the adoption that makes them stick. • Build and ship reusable deployment tooling and AI agents • Turn field patterns into product leverage. Package recurring customer needs into reusable assets and clear product/engineering input so each deployment gets faster and the platform gets stronger. • Turn field patterns into product leverage. • Your First 90 Days • Your First 90 Days • Days 1–30: Learn the platform deeply, shadow active deployments and sales calls, and meet the accounts you'll support. • Days 1–30: • Days 31–60: Scope a live opportunity alongside Sales, and run a deployment end to end as the technical lead. • Days 31–60: • Days 61–90: Surface and scope an expansion inside an existing account, and ship your first reusable deployment capability or internal AI agent. • Days 61–90:
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