Forward Deployed Engineer, AI

ClarityPay, Inc.; United States; On-site; USD 120,000 - 170,000; Full time

Job summary

ClarityPay, Inc. seeks Forward Deployed Engineers to embed with finance, credit, servicing, and operations teams, turning current workflows into dependable AI-native systems.

You’ll join the Data & AI organization, working with teams in New York and India, and collaborate with product and engineering to deliver external-facing AI solutions for merchants and partners. You will drive end-to-end delivery, define success criteria, and ensure adoption while maintaining strict governance and

Qualifications

• 2-5 years of software engineering or technical deployment experience with business users
• Production experience with LLMs: prompt engineering, agent or skill development, evaluation
• Strong Python and SQL for production code; low-code/automation tools helpful, not substitutes
• Data fluency in a lakehouse environment (Databricks, ClickHouse) for governed data pipelines
• High agency and comfort with ambiguity; able to autonomously choose what to automate

Responsibilities

• Embed with process owners to map work flow and define success criteria
• Own end-to-end delivery from discovery to production rollout and adoption
• Choose simple, dependable automation solutions and define task boundaries and ownership
• Write evaluations to verify outputs as data/models change
• Instrument builds with day-zero controls, thresholds, alerts, and owner accountability
• Build external-facing AI solutions for merchants/partners with product and engineering
• Codify reusable patterns and harness standards for faster future deployments
• Operate within AI controls, balancing automation with human review when needed
• Own the value case with impact hypotheses, baselines, and executive reporting
• Promote ClarityPay values and integrity across initiatives

Skills

Python

SQL

LLMs (prompt engineering)

Agent development

Data fluency

Tools

Databricks

ClickHouse

Retool

Zapier

n8n

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 automat