About Seev

We started by transforming how professional services firms write proposals. Today, Seev is becoming the AI layer for their entire revenue operation
Bootstrapped to 30+ customers, including market leaders like Telus, Levio, BPA
We've grown ARR >400% since Jan. 2026
Rapidly moving up-market with no signs of slowing down
Customers love how we combine pace of innovation and attention to detail

About This Role

You will join Seev's founding engineering team in a fundamentally full-stack AI engineering role. Most of the work is production backend systems: agent architecture, retrieval pipelines, and document processing.

We are a small, fast-moving team, so requirements are not always fully defined at the outset. You will need to build your own context, spot the gaps, and carry a feature from a rough idea through to something running in production that is used by real customers. We value ownership, clear communication, and hands-on execution over process for its own sake.

Role Requirements

5+ years of software engineering experience, including significant production backend work.
Strong Python, including async programming, Pydantic validation, testing, and API design (FastAPI or similar modern framework).
Hands-on experience shipping production features with Anthropic, OpenAI, Gemini, or comparable foundation-model APIs.
Experience building AI agents or multi-step LLM workflows: function calling, structured outputs, conversation state, context management.
Experience with document-grounded generation, including embeddings, vector/lexical search, and context assembly.
Working knowledge of AWS event-driven systems (Lambda, SQS, EventBridge, S3) and MongoDB or another document database.
Comfortable using AI-native dev tools (Cursor, Claude Code) to move fast without cutting corners on quality.

You Could Be a Great Fit If

You would rather own a feature end-to-end than hand off a spec and wait for someone else to build it.
You can take a vague, half-formed product idea and turn it into a shipped, working system.
You have debugged production LLM failures using logs, traces, and reproducible test cases.
You know when to write the test, add the eval, or improve the abstraction, and when those are a waste of time.
You communicate technical trade-offs clearly to non-technical teammates without dumbing them down.
You are comfortable being one of a handful of engineers, with no safety net of a large team behind you.

Bonus

Experience with Beanie ODM or MongoDB Atlas vector search.
Experience with document parsing, OCR, or programmatic DOCX/PDF/XLSX editing.
Familiarity with React and TypeScript.
Experience with Terraform, GitLab CI/CD, or production-monitoring tools.
Experience in RFPs, proposals, procurement, or other document-heavy B2B functions.

Who Should Not Apply

You want fully-specced tickets and a defined, repeatable process handed to you.
You need a large team, a deep bench, and an established platform before you can be productive.
You see AI-assisted coding tools as something to get around to rather than something you already rely on.
You would rather do research than ship production code that customers depend on.
You are uncomfortable with ambiguity, or with being wrong sometimes as you figure things out in real time.

Our Philosophy

We believe small teams of very talented people, given real ownership, outperform large teams with more process. We hire and compensate accordingly.
We value people who can seek answers independently and loop in the founders when they actually need to.
We would rather ship something real and improve it in production than perfect it in the abstract.
We value principled thinking and a strong sense of ownership over years of experience alone.

Benefits

Compensation includes salary + equity
Three weeks of PTO per year
Flexible work schedule
Remote-first

Seev

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