MeridianLink

• Own the end-to-end product strategy and roadmap for AI automation features within the platform. • Define measurable outcomes (efficiency gains, error reduction, decision accuracy) and hold the roadmap accountable to them. • Partner with executive leadership to align AI initiatives with company-wide product vision and revenue goals. • Build business cases justifying R&D investment based on expected revenue opportunities. • Identify high-value automation opportunities across the loan origination and account opening lifecycles. • Stay updated with the latest trends and advancements in AI and ML, to identify opportunities for innovation and incorporate relevant insights into product strategy and development. • Conduct deep discoveries with lending operations teams, compliance officers, and technology buyers to surface unmet needs. Dig beyond stated needs to define innovative solutions to customer challenges. • Synthesize qualitative research, quantitative usage data, and competitive intelligence into clear product opportunities. • Build and maintain a robust feedback loop with design partners and early-access customers during feature development. • Drive execution with engineering, data science, and design teams using agile practices and emerging Dev AI practices. • Translate business needs into clear and actionable epics, user stories, acceptance criteria, and supporting documentation. Lead rapid prototyping exercises to speed time to market and enable early customer validation. • Ensure requirements align with strategic business goals while balancing compliance, security, scalability, and technical feasibility. • Partner with data scientists and ML engineers to scope model development, define success metrics, and establish human-in-the-loop review processes. • Coordinate with compliance, legal, and information security to ensure AI features meet regulatory expectations (FCRA, ECOA, fair lending, model risk management, emerging regulation governing AI use in fintech). • Own launch readiness: go-to-market coordination, documentation, training, and customer communication. • Define and track KPIs for AI features post-launch: automation rates, exception volumes, model drift indicators, and business impact. • Establish feedback mechanisms that surface edge cases and model errors quickly and translate them into iteration cycles. • Communicate product performance transparently to leadership and stakeholders. • Collaborate with Sales and Marketing to track product pipeline development and revenue opportunity. Correct course where adoption falls short of expectation.

• Own the end-to-end product strategy and roadmap for the AI platform layer. • Partner with executive leadership to align AI initiatives with company-wide product vision and revenue goals. • Build business cases justifying R&D investment based on expected benefits. • Partner with principal engineers and ML infrastructure leads to make informed build-vs-buy-vs-partner decisions on foundational AI capabilities • Establish and govern platform-level standards: API versioning policies, model lifecycle management, prompt versioning, and observability requirements • Stay updated with the latest trends and advancements in AI and ML, to identify opportunities for innovation and incorporate relevant insights into product strategy and development. • Treat internal R&D teams as your primary customers. Conduct structured discovery with feature teams to understand their AI integration pain points, latency requirements, and data access needs. • Define and own the developer experience for consuming the AI platform: API contracts, SDK design, documentation standards, sandbox environments, and onboarding flows. • Establish a platform roadmap governance process: intake, prioritization, and communication of platform changes to dependent teams. • Build feedback loops with consuming teams post-release to detect friction, integration failures, and unmet capability needs early. • Establish monitoring and observability standards: model drift detection, confidence thresholds, input distribution shifts, and alerting policies • Translate regulatory requirements for AI use in lending (FCRA, ECOA, HMDA, OCC SR 11-7 model risk management) into concrete platform requirements: explainability APIs, audit logging, adverse action reason codes, and human-in-the-loop override mechanisms. • Maintain a clear capability matrix of which AI features are permissible for which customer tiers, regulatory environments, and data sensitivity levels. • Define and own platform-level SLOs: inference availability, P99 latency, pipeline throughput, and data freshness. • Build platform health dashboards and escalation playbooks for AI service degradation—distinct from application-layer monitoring. • Track platform adoption metrics: number of consuming teams, API call volumes, feature flag usage, and time-to-integrate for new consumers. • Hold regular platform reviews with engineering leadership to surface technical debt, capacity constraints, and architectural risks before they affect downstream feature teams. • Align platform metrics with those of the AI-based application products; collaborate with application Product Managers to ensure alignment.

• Lead implementation of complex software systems and services • Influence architecture and modernization initiatives • Mentor engineers through code reviews and technical coaching • Support operational excellence and production reliability • Partner closely with Product, QA, and DevOps teams

• Own implementation and delivery of product features and services • Collaborate on technical design and architecture discussions • Develop backend and frontend functionality using modern engineering practices • Contribute to CI/CD pipelines, testing, monitoring, and operational reliability • Grow technical expertise through mentorship and cross-functional collaboration

• Serve as technical lead, providing architectural direction and hands-on engineering for consumer lending solutions. • Design and implement robust, secure, and maintainable layers using C#, VB.Net, RESTful web services, and SOAP APIs, ensuring consistency and reliability across the consumer lending platform. • Own the full development lifecycle from requirements analysis and technical design through implementation, testing, deployment, and production monitoring. • Establish and enforce technical standards, coding practices, and integration patterns for the team; conduct code reviews and define quality gates that ensure security, performance, and maintainability. • Create architectural blueprints and technical design documents for complex problems, evaluating provider contracts, API specifications, and data schemas to guide the team toward sound, scalable solutions. • Act as the escalation point for production incidents and complex technical issues involving fintech integrations, driving root cause analysis and sustainable resolution. • Mentor and grow junior and mid-level engineers on the team; provide structured guidance, pair programming, and knowledge transfer on integration patterns, financial data handling, and platform conventions. • Evaluate emerging fintech integration partners and technologies, conducting proof-of-concept work and providing recommendations to engineering leadership. • Ensure all solutions meet MeridianLink's standards for data security, PII handling, regulatory compliance, and financial data privacy. • Contribute to cross-team architectural decisions affecting infrastructure, shared services, and platform-wide API strategy. • Perform other duties as assigned in a timely fashion.

• Managing large, complex datasets from multiple sources, ensuring they are accurate, clean, and organized for analysis. Perform detailed data wrangling tasks to handle data inconsistencies to prepare data for use in predictive models and analysis. • Implement advanced data transformation techniques (e.g., feature engineering, aggregation, normalization) to optimize data for specific machine learning, optimization and statistical models. • Work on various types of predictive models, including classification, regression, and clustering, using algorithms like decision trees, random forests, or neural networks. • Contribute to the fine-tuning of models by optimizing hyperparameters and evaluating performance using cross-validation, ensuring that models meet business and technical requirements. • Develop end-to-end analytical solutions, from data collection to model deployment, ensuring that the solutions meet the client's business objectives, such as improving lending strategies or underwriting decisions. • Ensure that the analytical results align with key performance indicators (KPIs) and help drive measurable outcomes. • Participate in the internal development of new data science methodologies that address evolving needs related to underwriting for our financial institution clients. • Present complex analytical findings in a clear and actionable format to internal stakeholders and external clients helping them interpret the results of predictive models and make informed decisions based on data insights. • Provide feedback and contribute to the continuous improvement of the data science workflow, ensuring projects are executed efficiently and with precision.

• Design, develop, and maintain secure, scalable Java-based backend services and applications supporting TAZWorks and InstaScreen platforms • Translate complex business requirements into well-designed technical solutions with a focus on reliability, performance, and maintainability • Take ownership of features and services through the full software development lifecycle, from design and implementation to deployment and support • Collaborate closely with Product Owners, Architects, DevOps, QA, and fellow engineers to deliver high-quality, business-driven solutions • Perform code reviews and provide constructive feedback to ensure adherence to coding standards, best practices, and architectural guidelines • Troubleshoot, debug, and optimize existing systems to improve performance, scalability, and security • Contribute to system design discussions, technical documentation, and proof-of-concept efforts for new approaches and technologies • Mentor and support junior and mid-level engineers, helping grow overall team capability and engineering excellence • Participate effectively in a distributed, agile scrum environment and contribute to continuous improvement initiatives

• Scope definition and control (complex deals): Lead drafting and negotiation of SOWs/change orders for complex and strategic engagements; define objectives, deliverables, assumptions, exclusions, timelines, and acceptance criteria to prevent misunderstandings and scope creep. • Performance and milestone governance: Define measurable success criteria (KPIs, acceptance criteria, milestones) and establish operating rhythms to track progress; surface portfolio trends/risks and drive timely issue resolution with delivery and business stakeholders. • Compliance and risk management: Set and apply contracting guardrails aligned to company standards and policies; identify legal/procurement/financial risks, frame tradeoffs, and escalate with recommended paths forward to enable informed decisions. • Change management: Own the governance model for scope, timeline, and deliverable changes; drive cross-functional alignment, document approvals, and maintain a clear audit trail across the engagement lifecycle. • Contracting strategy and enablement: Develop, standardize, and maintain templates, clause guidance, and negotiation playbooks; partner with stakeholders to improve cycle time, consistency, and risk posture across Professional Services deals. • Stakeholder communication and decision support: Synthesize complex issues into clear options, tradeoffs, and recommendations for senior stakeholders; proactively remove roadblocks, align decision-makers, and drive timely closure.

• Define the enterprise data architecture: Own the conceptual, logical, and physical data models for MeridianLink's analytical and operational data platform, including source-aligned, integrated, and consumption-ready layers. • Build the meta-model: Design and maintain a meta-model that captures entities, relationships, business definitions, ownership, lineage, sensitivity classifications, and SLAs — and make sure it is wired into our tooling, not stuck in a slide deck. • Drive the lakehouse strategy: Architect our medallion (bronze / silver / gold) Delta Lake patterns on Databricks; define standards for partitioning, clustering, schema evolution, slowly changing dimensions, and historical reproducibility. • Be hands-on: Write PySpark, SQL, and Delta Lake code. Build reference implementations, prototype patterns, review pull requests, and personally model critical domains rather than delegating every detail. • Lead data integration design: Set patterns for ingestion through Informatica Data Management Cloud (IDMC) and direct Databricks pipelines, including CDC, batch, streaming, and API-based sourcing from our SaaS products and third-party systems. • Champion data governance and lineage: Partner with data governance, security, and compliance leaders to operationalize cataloging, lineage, classification, masking, and access controls across the platform (Unity Catalog, IDMC, and adjacent tools). • Standardize data modeling practices: Establish the standards, naming conventions, and review processes used by the Data Engineering team. Coach engineers on dimensional modeling, Data Vault, and other techniques where they best fit the use case. • Partner across the business: Work closely with Product, Engineering, Analytics, ML, Finance, Risk, and Customer-facing teams to translate business needs into durable data designs. • Influence the roadmap: Identify gaps in tooling, capability, and skill; propose investments; and drive multi-quarter initiatives that materially improve how MeridianLink uses its data.

• Build and maintain vector stores for RAG: Design embedding pipelines, chunking strategies, indexing approaches, and refresh patterns for the vector stores powering retrieval-augmented generation across MeridianLink products. • Own the feature store: Design, build, and operate feature store assets used for model training and online/offline inference, including feature definitions, freshness SLAs, lineage, point-in-time correctness, and reuse across teams. • Design graph data structures: Build graph databases that model relationships between applicants, applications, products, lenders, decisions, and outcomes — and make them queryable for both AI use cases and analytical investigations. • Lead data discovery: Profile our lending, deposit, and behavioral datasets to identify hidden trends, segments, anomalies, and potential model drivers; turn findings into actionable hypotheses for product, risk, and growth teams. • Engineer for AI consumption: Build the curated, AI-ready datasets that downstream model builders, application engineers, and analysts rely on — with appropriate quality, documentation, and governance baked in. • Evaluate retrieval and feature quality: Define and run evaluation frameworks for RAG retrieval quality, feature drift, embedding quality, and graph completeness; iterate based on what the metrics tell you. • Partner with model builders: Work closely with ML engineers and applied scientists to make sure the data structures you build accelerate their work rather than slow it down. • Champion responsible data use: Partner with governance, security, and compliance to ensure that AI-facing data assets respect data classification, customer consent, and regulatory boundaries from day one. • Communicate findings: Translate discovery work into clear narratives — write-ups, notebooks, dashboards, and short presentations — that help non-technical stakeholders act on what the data is showing.