PayNearMe

• Lead sales engagements with Account Executives and Account Management to qualify new sales opportunities. • Serve as the sales team subject matter expert on PayNearMe’s technology platform, payments solutions, Agentic IVA capabilities, and services model in both internal and customer-facing engagements. • Partner with Account Executives to position, demonstrate, and sell PayNearMe’s Agentic IVA solution and future agentic AI modules as they are introduced to market. • Develop and conduct compelling demonstrations of PayNearMe’s payments platform, Agentic IVA, and related AI-powered solutions for prospective customers. • Propose, scope, and architect solutions that transform the payment and customer engagement experience. • Collaborate with Product and Marketing teams to translate product functionality and innovation into compelling business value propositions. • Solicit and document client feedback and evaluate market data to help shape sales strategies, product direction, and AI solution adoption. • Provide feedback on market insights, competitive trends, and emerging opportunities in payments, customer engagement, and agentic AI technologies. • Achieve sales goals and quotas on a quarterly and annual basis. • Work with the sales team to establish relationships with customer contacts in technical, operational, product, and business leadership roles, building credibility with all decision makers. • Work closely with the onboarding team to initiate new implementations and ensure all relevant solution designs are documented and complete. • Assist the sales team with responding to technical RFP, RFI, and security questionnaire requirements. • Work with the sales team to document product gaps identified during the sales process and communicate those gaps to Product and Engineering prior to contract signature. • Act as a trusted advisor to customers throughout the evaluation process, helping them understand how PayNearMe’s payments and AI solutions can drive measurable business outcomes.

• Define the long-term vision for data as a strategic product and platform capability. • Identify opportunities to improve products and customer experiences through data. • Prioritize investments across data products, insights, analytics, recommendations, and analytical AI capabilities. • Set roadmap priorities that keep data strategy aligned to business goals. • Define reusable data capabilities that scale across products and experiences. • Drive accessibility, governance, discoverability, and common business definitions for core data assets. • Establish a consistent approach for modeling, measuring, and exposing data across the platform. • Enable teams to build on shared data assets rather than one-off solutions. • Identify and prioritize opportunities for customer-facing insights, recommendations, and analytics experiences. • Embed data-driven capabilities into existing products. • Evaluate and pursue new data-driven product opportunities. • Own the roadmap for analytical AI capabilities that turn raw data into decisions. • Define the data, measurement, and product requirements behind AI-enabled experiences and workflow automation. • Shape how data improves decision-making, automation, and customer outcomes. • Translate emerging technology capabilities into real customer and business value. • Data operates as a reusable platform capability across multiple products and experiences. • Product teams consistently discover, access, and trust core data assets. • Data-driven insights and recommendations are embedded within customer experiences. • Data investments tie directly to customer outcomes and business impact. • New opportunities for differentiation and growth emerge through data. • The company has a clear strategy for data across analytics, recommendations, automation, and AI.

• Assist senior engineers in the design of data models and schemas, and participate in code walk-throughs. • Support performance tuning efforts on SQL queries regarding applications, storage, and network. • Explore and utilize AI-powered tools and automation solutions to increase efficiency in DataOps workflows and reduce manual toil. • Follow established processes for data quality, data governance, testing, and on-going monitoring. • Assist in the analysis of current systems and support the implementation of database designs to support our business applications. • Adhere to established data models, modeling naming standards, abbreviations, guidelines, and best practices. • Assist in translating logical data models and implementing physical database structures. • Monitor data service costs and flag potential cost inefficiencies to senior team members. • Maintain and update documentation for database architectures.

• Develop code using Ruby on Rails • Build reusable tasks to reduce the flow of engineering work from support • Build maintainable ISVs to connect our systems to third parties • Add critical features to our APIs • Assist onboarding of new clients and help solve escalations • Be instrumental in building the next generation of internal tools and services • Write specs for automated coverage of your code. (We have over 95% coverage) • Code review other developers' changes as they will review yours

• Provide technical leadership in the design, development, and evolution of PayNearMe's modern data platform and data products. • Work closely with Product, Engineering, Risk, Finance, Operations, and Analytics teams to model complex payment data into trusted, governed, and reusable data products that power reporting, analytics, operational intelligence, AI, and customer-facing applications. • Lead the design of scalable data models across the Bronze, Silver, and Gold layers of our lakehouse architecture while establishing engineering best practices and mentoring other engineers through technical leadership. • Collaborate with business stakeholders, product managers, and engineering teams to understand business processes and translate them into well-designed analytical data models. • Develop reusable semantic models that provide consistent business definitions and metrics across the organization. • Capture and transform transactional payment data from operational systems into curated analytical datasets. • Optimize query performance and execution.

• Define and lead the technical approach for modernizing software quality across our engineering organization, evolving from QA-as-gatekeeper toward a shared ownership model • Assess our current QA processes, test automation, manual testing practices, release validation, and tooling, then develop a practical roadmap for reaching successive levels of maturity • Propose and implement tooling, workflows and practices for building quality into the agentic SDLC • Establish a long-term vision for quality management that reduces handoffs and allows development pods to operate with autonomy • Partner closely with proddev leadership, SGRC, QA, and performance testing teams to align quality management practices with business risk, regulatory needs, and delivery goals • Define, track, and report the metrics that will shape decisions, such as coverage and defect escape rates • Define a modern test pyramid with clear definitions, ownership, and goals for unit, integration, contract, end-to-end, exploratory, and production validation layers, along with specialized considerations such as performance and security • Improve the reliability, speed, maintainability, and signal quality of our automated test suites; guide appropriate use of existing tools while identifying opportunities to improve, replace, or supplement them • Develop strategies for test data management (seed, synthetic, and production-like sets), environment reliability, contract testing, service virtualization, synthetic monitoring, and automated smoke testing • Identify opportunities to leverage agentic AI in test generation & scenario discovery, and design workflows and skills for multi-agent analysis of generated code • Design quality gates that are risk-based, automated where possible, and integrated into CI/CD pipelines • Improve sandbox and production smoke testing so that post-deploy validation becomes faster, more automated, and more reliable, supporting a fast-rollback strategy when needed • Collaborate with teams to define what “ready to release” means for different types of changes, services, and risk profiles • Provide technical mentorship to QA engineers, software engineers, and engineering leads. • Teach modern quality engineering practices through pairing, workshops, documentation, architecture reviews, and hands-on examples. • Build buy-in and drive change across teams by showing practical improvements rather than imposing abstract process changes. • Help cultivate a culture where quality is owned broadly by engineering, supported by QA expertise, and embedded throughout the delivery lifecycle. • Approach change with empathy, especially for team members whose current roles and responsibilities will evolve.

• Scope, design, build, and maintain APIs, services, and workflows that reliably process high-scale money movement and settlement. • Own the architectural direction for the settlement domain in partnership with the CTO; produce and maintain architecture documentation (current state, target state, and migration plan) and drive alignment across teams. • Review and approve architecture decision records (ADRs) and technical designs for settlement-related changes; ensure decisions meet standards for correctness, operability, security, and long-term maintainability. • Drive end-to-end execution for large, ambiguous initiatives: reduce risk early, align stakeholders, and deliver in phases. • Improve correctness and operational safety across settlement flows: deeply understand how money moves end-to-end (timing, state transitions, adjustments, exceptions), design for edge cases, and implement idempotency, reconciliation guarantees, safe backfills, incident readiness, and disciplined releases. • Partner with Engineering Manager, Product, Performance, CTO, and other Staff peers to shape roadmaps and strategy for settlement—develop deep expertise in both the technical system and the business needs (what clients require from settlement, reporting, timing, and exception handling) and translate that into durable platform capabilities. • Raise engineering standards across the team and broader org by partnering with other Staff engineers to define and drive consistent patterns for building and operating money- movement systems (design reviews, abstractions, testing strategy, observability, and operational practices) across monolith + microservices. • Drive adoption of AI-assisted engineering practices (tooling, workflows, guardrails) to improve throughput and quality—especially for testing, incident response, code review, and documentation.

• You'll define and build the agent platform used across applications at PayNearMe—the architectural patterns, the shared infrastructure—and you'll set the bar for how agents are designed, tested, evaluated, and operated in a regulated, money-movement context. • Own the architectural direction for agentic AI at PayNearMe in partnership with other engineering leaders. We are building an agent platform, not a single agent—our business customers have different rules, brand voices, allowed actions, knowledge bases, and compliance postures, and the architecture has to treat per-tenant configuration, isolation, and evaluation as first-class concerns. • Produce and maintain architecture documentation (current state, target state, migration plan) and drive alignment across product, engineering, security, and compliance. • Design, build, and ship production agents—including voice and chat agents for a wide range of payment-related activities—that integrate cleanly with our Ruby on Rails / MySQL platform and partner services (ElevenLabs, Twilio, and others). • Treat tool design as a first-class discipline: tool schemas, descriptions, idempotency, side-effect semantics, and error surfaces directly determine agent quality, and for money-moving tools they determine whether we can stand behind every action the agent took. • Make and defend the "what kind of intelligence goes where" decisions: when to lean on a partner's stack vs. orchestrate frontier LLMs directly, when RAG is the right answer vs. tool calls vs. fine-tuning, when a small/fast/cheap model is sufficient vs. when a frontier model is warranted, and where classical ML or deterministic logic is a better fit than an LLM at all. • Design the seams that let us swap providers, voice vendors, and models as the landscape shifts—without rewriting the agents that sit on top of them. • Design and operate the agent lifecycle as a closed loop: testing, offline evals, online evals, observability, scoring, and a disciplined feedback path from what we observe in production back into the test suite and eval set. • Own the rollout discipline for non-deterministic systems: prompt and agent versioning, shadow mode, canary-by-tenant, gradual ramps, and rollback playbooks that account for the fact that the "bad version" may have already taken real payments. The system has to get measurably better over time, not just ship. • Own the unit economics of agent interactions. Token budgets, prompt and semantic caching, model cascades (cheap model first, escalate on uncertainty), batch APIs, latency-vs-cost tradeoffs, and per-tenant cost attribution should be instrumented and reasoned about explicitly—at scale, the gap between a well-engineered conversation and a naive one is the business. • Build the guardrails that make agents safe in a payments context: scope enforcement, refusal behaviors, deterministic handoffs for anything money-changing, PCI-compliant handling of card data, PII protection, and clear human-in-the-loop or fallback paths when confidence is low. • Own the identity and consent model for agent-initiated actions—who the agent is acting as, when step-up authentication is required before a consequential action, and how explicit consent is captured and stored in a form that holds up in a dispute or chargeback. • Treat prompt injection and social-engineering of the agent as real attack surfaces; stand up a red-team practice that exercises them continuously, especially against money-moving tools. • Treat voice as its own modality, not a text agent with a microphone—design for latency budgets, barge-in and turn-taking, STT/TTS error modes, DTMF fallback, recording and consent, and the operational realities of telephony. • Partner with Security, Compliance, and Legal to ensure agent behavior meets PCI-DSS, state-level payments regulations, and our customers' own compliance obligations. Make agent decisions reconstructable: for any consumer interaction we should be able to explain to a regulator, an auditor, or a disputing party exactly why the agent did what it did, on what information, and with what authorization. • Raise the bar across the org for agent engineering: define shared patterns for prompts, tools, evals, telemetry, and incident response; serve as a reviewer and approver for architecture decision records (ADRs) and major designs in the agent domain. • Teach the rest of engineering how to build, evaluate, and operate agents—most engineers on the team are picking this discipline up for the first time, and the team's velocity depends on how well that knowledge transfers. • Partner with the Engineering Managers, Product, and other Staff peers to shape the roadmap—develop deep expertise in both the technical system and the business need (what our customers and their consumers actually want from an agent), and translate that into durable platform capabilities.

• Partner with stakeholders across all business functions to understand current workflows, identify pain points, and design improvements. • Translate ambiguous business requirements into clear, scalable technical solutions — from quick automations to multi-system integrations. • Champion a ship-and-iterate mindset: get a working solution in place, learn from it, and improve over time rather than waiting for perfect requirements. • Configure and maintain connected business systems (Salesforce, Jira, n8n, Dataiku, Snowflake, and others) to accurately reflect current business processes. • Build and maintain integrations and automations between systems using APIs, low-code platforms (e.g., n8n), and declarative tools. • Support quote-to-cash initiatives, including pricing data management, contract lifecycle tracking, billing automation, and other functions foundational to scaling & future-proofing the business. • Evaluate and onboard new SaaS tools in coordination with IT, Security, and business stakeholders. • Assist with environment management, configuration documentation, and release coordination for internal business systems. • Maintain clear documentation of configurations, integrations, and processes to ensure continuity and enable team collaboration. • Provide training and support to end-users on system functionality and best practices. • Communicate completed work and business impact broadly — we celebrate our wins and build visibility for the team.

• Leadership: This role will lead a team of Account Executives, building the strategy around the sales team’s approach to the auto and personal lending market. • Pipeline Strategy & Oversight: Own the pipeline healthy by setting prospecting standards, monitoring funnel conversion metrics and ensuring sufficient pipeline coverage. • Drive Revenue Growth: Drive consistent attainment of team sales targets by removing obstacles and ensuring disciplined execution across the full sales cycle. • Pricing & Deal Governance: Provide oversight and approval on pricing strategy, deal structuring, and contract terms. • Develop Market Insights: Provide feedback to inform go-to-market strategies and influence product positioning. • Establish High-Level Relationships: Work closely with C-level executives, decision-makers, and stakeholders. • Cross Functionality Leadership: Collaborate with business development, marketing, product, and customer success teams to align on demand generation, messaging, and onboarding. • Partnership support: Work closely with our Partnership Team to continue to deepen partner relationships, supporting joint go-to-market initiatives.