AI Solutions Engineer

V7 LLC
New York, NY, United States
about 1 month ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Compensation
$180,000.0 - $230,000.0
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Python (Programming Language) Systems Integration Large Language Models Prompt Engineering Data Pipelines

Job description

  • You’ll join our go-to-market team as our second Solutions Engineer in New York (the team is 3 people), sitting right at the front of the sales cycle in a company processing tens of millions of documents for customers across finance, insurance, and real estate.
  • You’ll be the technical partner to our Account Executives, the person who turns a promising conversation into a bought-in technical champion and a closed deal.
  • V7 Go 4x-ed revenue last year, with 160%+ upsell into accounts. You’ll help accelerate that by winning the technical evaluation on every deal you touch.
  • We run a lean, high-trust team where you’ll work directly with AEs, engineers, and product to move complex deals over the line.
  • Your work directly shapes how enterprises experience agentic AI for the first time and how quickly they believe in it.

What you’ll be doing from day one

  • Partner with Account Executives across the sales cycle: lead technical discovery, run demos tailored to each buyer, and shape the technical win strategy on every deal.
  • Own proofs of concept end to end, from scoping success criteria with the customer through to building the prototype in V7 Go and presenting results back to technical and business stakeholders.
  • Handle technical qualification, objection handling, and security or architecture questions that come up during evaluation, and respond to RFPs where needed.
  • Build working prototypes with prompt engineering, data pipelines, and integrations to prove out real customer use cases during the sales process.
  • Be the trusted technical voice in the room, translating a customer’s problem into a clear solution vision that maps to business value.
  • Feed patterns and objections from live deals back to product and engineering so we sharpen both the product and the pitch.

Requirements

  • You have presales or sales engineering experience, ideally selling technical products into enterprise, and you know how to win a technical evaluation.
  • You are comfortable owning the technical side of a deal alongside an AE: discovery, demos, POCs, and the stakeholder management that comes with a complex sales cycle.
  • You are a prototyper at heart with a gift for talking to customers, building relationships, and solving technical problems quickly and repeatably.
  • You love coding with Python.
  • You have experience delivering Large Language Model projects with customers, including LLM API integration, up-to-speed knowledge of foundation models, solutions design and architecture, prompt engineering, and/or measuring AI accuracy.
  • You can develop and articulate an AI solution vision to technical and business stakeholders, matching the value proposition to business needs.

About the company

At V7, we’re building AI platforms that help humans do their best work, at incredible scale and speed. Our mission is to turn human knowledge into trustworthy AI, making complex tasks faster, smarter, and more accurate. We’re growing fast, backed by leading investors and AI pioneers (including the minds behind Transformers and Gemini).

The product

V7 Go is a platform for building and deploying custom AI agents, made for the document-heavy work that finance and insurance teams handle every day. It takes multi-modal data and returns verifiable outputs with transparent logic, so accuracy and compliance come built in. It runs on the latest models too, including GPT, Claude and Gemini. Have a look at what we’re building for finance and insurance.

Most AI agents still behave like black boxes. They wander off-task, miss the context that matters, and make calls you can’t check. Go Agents are different: predictable, transparent, and auditable at every step.

  • Build custom AI workflows with controlled analysis at every stage. Every output comes with citations and a full audit trail, so the human in the loop can see exactly how it got there.
  • The Context Graph makes all your data (across workflows, documents and integrations) queryable in one place, giving your agents the context to be properly useful. It’s your institutional memory.

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