Jobs at Quantifind

Own the product strategy and roadmap for machine-learning-powered core models. Translate customer needs into model objectives, evaluation criteria, thresholds, requirements, and acceptance criteria. Partner with Data Science and Engineering to deliver and improve models, assess performance, document capabilities and limitations, support customer evaluations, and manage model risk requests. Serve as a customer-facing subject matter expert while driving measurable outcomes and executive alignment.

Lead end-to-end technical software implementations, translate business requirements into development initiatives, manage cross-functional teams, track risks/issues and project financials, define requirements and milestones, and report progress to stakeholders and senior leadership to ensure timely, high-quality delivery.

Build responsive browser-based applications using React, Redux, JavaScript, HTML, and CSS. Develop reusable components, data visualizations, REST API integrations, unit and integration tests, and maintainable code for continuous delivery. Collaborate with Design, DevOps, Product, Data Science, and platform engineering teams in an Agile environment to deliver customer-facing risk visualization features.

Develop machine learning and data science solutions for financial crime risk management. Responsibilities include analyzing large datasets with SQL, Python, and PySpark; designing ETL pipelines; training supervised and unsupervised models; implementing solutions in Scala; applying LLMs and NLP; supporting customer proofs of concept; and collaborating with product and platform teams in an agile environment.

Solutions Consultant responsible for designing, configuring, and presenting AI-driven risk management solutions to financial institutions and government clients. The role involves customer discovery, solution design, business case development, executive workshops, proposals, demos, implementation planning, and cross-functional collaboration with Product, Engineering, Marketing, and Customer Success. Candidates should understand AML, KYC, sanctions, fraud, and financial institution workflows, while articulating measurable business value and supporting sales engagements through production.

Develop and productionize statistical and machine learning solutions for financial crime detection. Responsibilities include NLP, entity resolution, document classification, knowledge graphs, topic modeling, scalable ML pipelines, large-scale data analysis, and translating business challenges into quantitative problems. The role collaborates with Product, Platform Engineering, Sales, and clients, requiring clear communication with technical and non-technical audiences.

Manage and scale infrastructure across data centers and AWS using infrastructure-as-code, automation, and configuration management. Build and operate secure, maintainable deployments involving Kubernetes, EKS, containers, databases, firewalls, and AWS-native services. Improve system performance, observability, testing, and operational processes while collaborating with DevOps, platform, security, data science, and product teams. Lead major projects, mentor engineers, document solutions, and promote Agile engineering best practices in a regulated environment.

Own the Graphyte platform’s enterprise-readiness roadmap, including identity and access management, permissions, auditability, tenancy, data handling, APIs, developer experience, and administrative controls. Drive performance, scalability, reliability, and resilience outcomes with engineering. Translate security, compliance, procurement, and customer requirements into product priorities and technical requirements. Partner with Compliance, Delivery, Customer Success, and executives to improve enterprise adoption and deployment success.

Lead strategic customer success for large financial institution clients using AI-driven fintech SaaS solutions. Manage onboarding, adoption, renewals, expansions, and client advocacy while serving as the primary executive contact. Collaborate with Product, Engineering, Data Science, Marketing, and Sales to align client needs with the product roadmap, improve engagement processes, and ensure delivery against project and contractual commitments. Occasional travel to client sites is required.

Manage strategic relationships with enterprise financial institution clients, driving onboarding, adoption, satisfaction, and long-term success for AI-driven SaaS solutions. Coordinate with Customer Success, Product, Data Science, Engineering, and Sales teams to deliver customer commitments within project and contractual timelines. Provide client presentations, training, support, expectation management, and advocacy throughout the customer lifecycle.

Manage strategic enterprise customer relationships for AI-driven SaaS solutions serving financial institutions and public-sector clients. Responsibilities include onboarding, customer presentations and training, ongoing support, expectation management, and advocacy throughout the client lifecycle. Partner cross-functionally with Customer Success, Product, Data Science, Engineering, and Sales teams to meet customer commitments, project milestones, and contractual timelines while driving satisfaction and measurable client value.

Design, build, and operate scalable, secure infrastructure using IaC principles. Automate deployments, manage on-prem and cloud fleets, improve observability and performance, and collaborate with cross-functional teams to productionize services and ML modules.

Design enterprise, data-heavy product surfaces from research through high-fidelity specs. Lead features, run customer sessions, improve workflows and data visualizations, scale the design system, and collaborate with Product and Engineering. Leverage AI tools to accelerate research and prototyping.

Manage and automate infrastructure for on-prem and cloud deployments using infrastructure-as-code. Build, deploy, and operate services (containers, orchestration, fleet management), improve observability and availability, support security practices, and collaborate with cross-functional teams to scale and operationalize ML and web applications.

Build and maintain Quantifind's Apache Spark batch pipeline and platform services to generate risk signals from diverse data. Design scalable algorithms and data structures, implement ETL pipelines, microservices and REST APIs, deploy on Kubernetes, collaborate with Data Science, DevOps and Product, and ensure robust automated unit and integration testing of distributed systems processing terabytes daily.

Work directly with government and enterprise clients to architect, integrate, and deploy secure, compliant AI-enabled cloud environments. Build microservices and APIs, manage IaC, CI/CD, containers/orchestration, observability, and platform uptime across commercial and government clouds while meeting accreditation standards.

Build and maintain platform data services and ML infrastructure powering a SaaS product. Work on Hadoop/Spark pipelines, real-time services, Kubernetes/Docker containerized microservices in Scala/Python, design scalable algorithms and ETL pipelines, write automated tests, and collaborate with data scientists, DevOps, front-end engineers, and product managers.

Manage and automate infrastructure as code for datacenter and cloud deployments. Build, deploy, and maintain scalable, secure Linux-based systems using containers, orchestration, configuration management, monitoring, and release engineering tools. Collaborate with platform, data science, security, and product teams to improve reliability, performance, and deployment practices.

Design, implement, and operate secure, high-performance networks for Internet-facing SaaS and API services. Perform security assessments, incident triage and response, firewall and device log analysis, and monitoring improvements. Automate network configuration and policy management (Ansible, Python), support cloud and data center deployments, mentor engineers, and participate in availability, disaster recovery, and compliance activities.