Job description

About Us: At ClarityPay, we’re redefining the point-of-sale credit market to bring more value to merchants. Based in NYC and Atlanta, our fast-growing fintech empowers large merchants with configurable “Pay-Over-Time” tools— including monthly installments, BNPL, and revolving products. We solve complex credit challenges with speed, precision, and intelligence. Our values guide everything we do: we put merchants first, stay data-driven, always know the why, learn relentlessly, and win together as a team. Role Overview: ClarityPay is seeking Forward Deployed Engineers to embed with the teams that run the company and turn how they work into dependable, AI-native systems. You will sit with process owners in finance, credit and portfolio risk, servicing and collections, and operations; scope the problem with them before building; ship agents, controls and automations into production; and stay through adoption as the named secondary owner of the process. You will join the Data & AI organization, working alongside engineers in New York and India on a shared platform, and alongside Product and Engineering you will build external-facing AI solutions for merchants and partners on the ClarityPay platform. You will use AI tooling to make the build the easy part, and you will be measured on what is adopted and relied upon, not on what is demoed. Key Responsibilities: - Embed with process owners to map how the work actually runs, scope the problem with them before building, and agree success criteria and a baseline up front. - Own delivery end to end — discovery, technical scoping, system design, build and production rollout — and stay through adoption as the named secondary owner of the process. - Choose the simplest dependable solution: simplify or remove a step before automating it, then pick a scheduled job, a triggered job, or a bounded agent with a defined mandate, action set, escalation owner and pause conditions. - Write evaluations for everything you ship: prove the output is correct before a team depends on it, and re-check it as models, data and processes change. - Instrument what you build, and the processes around it, with detective (“day-zero”) controls — thresholds, exception inventories and alerting, each with an action path and a named owner — so a breach surfaces the same day, not at month-end. - Build external-facing AI solutions for merchants and partners with Product and Engineering; anything external ships through Engineering’s review and deployment standards. - Codify reusable patterns, evals and harness standards so the second deployment is faster than the first; prefer reusable capabilities over one agent per task, and bring field signal back to the platform roadmap. - Work inside ClarityPay’s controls for AI — evaluate before exposure, bound the exposure, keep a person at the gate — and propose where a human must review, where automation may act unattended, and what gets logged. - Own the value case: set impact hypotheses and baselines, measure before and after, and report to executives on business impact, total effort saved (including review and maintenance), control quality and reuse. - Continually promote and apply ClarityPay values; be a role model of trust and integrity. What Success Looks Like in Your First 6–12 Months: - Desktop and spreadsheet-based reporting processes are inventoried, baselined and moved onto AI agentic systems running on governed data, with a person at the gate where judgment is needed. - Recurring performance reporting — portfolio, collections, servicing and finance — is produced by agents from governed metrics rather than manual pulls, on time and traceable to source. - Day-zero controls are live in your first processes, each with a threshold, an action path and a named owner, so breaches surface the same day. - The effort returned is measured against the baseline, net of review, exceptions and maintenance, and reported to executives. - At least one external-facing solution for merchants or partners is in production and adopted. - The patterns, evals and agent-harness standards you built are reused by the next deployment. Qualifications: - Curious, energetic, positive, solution-oriented, collaborative, trusted. - 2–5 years of software engineering or technical deployment experience that includes working directly with business users — in operations, business systems, solutions engineering, or technical consulting. - Production experience with LLMs: prompt engineering, agent or skill development, and evaluation. Things other people depend on, not demos. - Strong Python and SQL: you write and review production code and use AI-assisted development to move faster. Low-code and automation platforms (e.g., Retool, Zapier, n8n) are useful, not a substitute. - Data fluency in a lakehouse environment (e.g., Databricks, ClickHouse): pull answers out of governed data, build reliable pipelines, and catch when the output is wrong. - High agency and comfort with ambiguity — you will choose what to automate as often as you are told. - Strong discovery and communication skills: able to sit with a collections specialist or a finance analyst, understand their workflow, and explain trade-offs to a non-technical audience; comfortable working across time zones with engineering colleagues in India. - Judgment about what should not be automated — where the cost of being wrong outweighs the time saved. Nice to Have: - Fintech, lending, payments, banking, or another regulated environment. - Experience building MCP servers, sub-agents, or agent skills. - MLOps or experimentation experience: evaluation frameworks, staged A/B tests, model monitoring. - Prior forward-deployed, professional services, or founding-engineer experience. How You’ll Know This Role Is Right for You: - You see manual, repetitive work and cannot leave it alone. - You would rather ship a working automation this week than design the perfect one this quarter. - You have receipts — things you actually built that other people now depend on. - You want range: collections one month, month-end close the next, portfolio risk monitoring after that. - You would rather stay through adoption than hand off at launch. What We Offer: - Base salary range: $145,000–$195,000, plus variable compensation and equity. - Comprehensive benefits (medical, dental, vision). - 401k program. Ready to redefine point-of-sale financing with us? Apply today and join a passionate team committed to making financial clarity a reality. ClarityPay is an equal opportunity employer. We do not discriminate based on race, ethnicity, color, ancestry, national origin, religion, sex, sexual orientation, age, disability, veteran status, marital status, or any other legally protected status.

Requirements

Must-haves

Embed with process owners to map how the work actually runs, scope the problem with them before building, and agree success criteria and a baseline up front.
Own delivery end to end — discovery, technical scoping, system design, build and production rollout — and stay through adoption as the named secondary owner of the process.
Write evaluations for everything you ship: prove the output is correct before a team depends on it, and re-check it as models, data and processes change.
Build external-facing AI solutions for merchants and partners with Product and Engineering; anything external ships through Engineering’s review and deployment standards.
Codify reusable patterns, evals and harness standards so the second deployment is faster than the first; prefer reusable capabilities over one agent per task, and bring field signal back to the platform roadmap.
Work inside ClarityPay’s controls for AI — evaluate before exposure, bound the exposure, keep a person at the gate — and propose where a human must review, where automation may act unattended, and what gets logged.
Own the value case: set impact hypotheses and baselines, measure before and after, and report to executives on business impact, total effort saved (including review and maintenance), control quality and reuse.
Continually promote and apply ClarityPay values; be a role model of trust and integrity.
What Success Looks Like in Your First 6–12 Months:
Desktop and spreadsheet-based reporting processes are inventoried, baselined and moved onto AI agentic systems running on governed data, with a person at the gate where judgment is needed.
Recurring performance reporting — portfolio, collections, servicing and finance — is produced by agents from governed metrics rather than manual pulls, on time and traceable to source.
Day-zero controls are live in your first processes, each with a threshold, an action path and a named owner, so breaches surface the same day.
The effort returned is measured against the baseline, net of review, exceptions and maintenance, and reported to executives.
At least one external-facing solution for merchants or partners is in production and adopted.

Nice-to-haves

Fintech, lending, payments, banking, or another regulated environment.
Experience building MCP servers, sub-agents, or agent skills.
MLOps or experimentation experience: evaluation frameworks, staged A/B tests, model monitoring.
Prior forward-deployed, professional services, or founding-engineer experience.
How You’ll Know This Role Is Right for You:
You see manual, repetitive work and cannot leave it alone.
You would rather ship a working automation this week than design the perfect one this quarter.
You have receipts — things you actually built that other people now depend on.
You want range: collections one month, month-end close the next, portfolio risk monitoring after that.
You would rather stay through adoption than hand off at launch.

Application prep

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

Keywords to mirror

aipythonsql

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k