Job description

About Obin AI has made plenty of promises. Regulated financial institutions need systems they can deploy, govern, and defend to a regulator. Obin is the agentic workforce for regulated finance. We build AI agents trained on how a specific firm actually works — its operating logic, its edge cases, its regulatory framework — that run inside the client's own cloud and governance boundary. Every agent we build belongs to the client: their cloud, their model, their IP. Outputs are auditable, traceable, and reversible. We help leading institutions rethink critical workflows across insurance, private credit, private equity, and commercial lending. Our founders are Apoorv Saxena (Global Head of AI at JPMorgan Chase, Managing Director at Silver Lake, Head of Product for Cloud AI at Google) and Lak Lakshmana (Data & AI operating executive at Silver Lake, Head of Data Analytics & AI at Google). Our advisors include Fei-Fei Li and Lukasz Kaiser. We are in production at institutions representing over $1 trillion in assets, profitable since seed, and closing a Series A. The role You are our first sales engineer. Today our CTO and our founders sit in every technical conversation, which does not scale and is the reason this seat exists. You will be the technical partner to our Client Partners and Account Executives across the full cycle: discovery, demo, architecture and security review, workflow scoping, and the handoff into delivery. You will translate in both directions — turning a client's operating problem into something our engineers can scope, and turning what we have built into something a business buyer understands. Be clear-eyed about what this is. No team, no library of demo assets, no discovery framework waiting for you. You will build the technical sales function while carrying it. What you will own • Technical discovery. Get underneath the stated requirement to the actual workflow, the exception paths, and where the current process breaks. • The business-buyer demo. Own the flow, the narrative, and the data, including the Context Studio story. Build it so a Client Partner can run it without you. • Architecture and security reviews. Lead conversations on data access, deployment model, controls, lineage, and auditability. • Model risk and compliance. Support the questions that stall deals in regulated institutions. • Accuracy and evals. Explain, credibly and specifically, how we get from a frontier model's baseline to production accuracy — ontology, encoded SOPs, edge-case handling, error correction, and the eval sets that prove it. Technical buyers will test this, and hand-waving ends the conversation. • Scoping with engineering. Translate opportunities into estimates our delivery team can stand behind, and be honest with sellers about what capacity allows. You produce the opportunity brief, the ROI validation, and the scope that feeds the SOW. • Clean handoff. The delivery pod inherits your scope. Make it something a forward-deployed engineer can build from without re-discovering the account. • Technical enablement. Build the objection library, the proof points, and the talk tracks so the commercial team is not dependent on you for every call. What we are looking for Must have • Enterprise sales engineering experience. You have supported seven-figure deals with long cycles and technical buying committees. • Financial services fluency. You have been through security review, model risk, and third-party risk at a bank, insurer, or asset manager, and you know what actually blocks a deal. • You can demo to a business buyer. Not just a technical audience. You know the difference and you can do both. • Applied AI understanding. LLMs, RAG, agentic workflows, and where each is and is not appropriate. Enough depth to be credible with engineers, and enough honesty to say what we cannot do. • Builder appetite. Comfortable with no playbook, no demo library, and no team. • You have killed a deal on technical grounds, against sales pressure, and you can explain why you were right. Strongly preferred • 5 to 12 years in sales engineering, solutions consulting, or forward-deployed engineering • Insurance, private credit, or asset management specifically • Hands-on ability to build a prototype or manipulate data rather than only presenting • Deployments into a client's own cloud (GCP especially), on-prem, or VPC-isolated environments • Experience in a services-plus-product model rather than pure SaaS • Early-stage or first-SE experience

Requirements

Must-haves

You are our first sales engineer. Today our CTO and our founders sit in every technical conversation, which does not scale and is the reason this seat exists.
Be clear-eyed about what this is. No team, no library of demo assets, no discovery framework waiting for you. You will build the technical sales function while carrying it.
What you will own
Technical discovery. Get underneath the stated requirement to the actual workflow, the exception paths, and where the current process breaks.
The business-buyer demo. Own the flow, the narrative, and the data, including the Context Studio story. Build it so a Client Partner can run it without you.
Architecture and security reviews. Lead conversations on data access, deployment model, controls, lineage, and auditability.
Model risk and compliance. Support the questions that stall deals in regulated institutions.
Clean handoff. The delivery pod inherits your scope. Make it something a forward-deployed engineer can build from without re-discovering the account.
Technical enablement. Build the objection library, the proof points, and the talk tracks so the commercial team is not dependent on you for every call.
What we are looking for
Enterprise sales engineering experience. You have supported seven-figure deals with long cycles and technical buying committees.
Financial services fluency. You have been through security review, model risk, and third-party risk at a bank, insurer, or asset manager, and you know what actually blocks a deal.
You can demo to a business buyer. Not just a technical audience. You know the difference and you can do both.
Applied AI understanding. LLMs, RAG, agentic workflows, and where each is and is not appropriate. Enough depth to be credible with engineers, and enough honesty to say what we cannot do.
Builder appetite. Comfortable with no playbook, no demo library, and no team.
You have killed a deal on technical grounds, against sales pressure, and you can explain why you were right.
5 to 12 years in sales engineering, solutions consulting, or forward-deployed engineering
Insurance, private credit, or asset management specifically
Hands-on ability to build a prototype or manipulate data rather than only presenting
Deployments into a client's own cloud (GCP especially), on-prem, or VPC-isolated environments
Experience in a services-plus-product model rather than pure SaaS
Early-stage or first-SE experience

Nice-to-haves

Strongly preferred

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigcp

Platform: gem
Effort: medium