Top Tech Jobs & Startup Jobs

Develop and support scalable Java applications, REST APIs, microservices, and cloud-native solutions on Microsoft Azure. Build containerized workloads with Docker and AKS, automate infrastructure using Terraform, and integrate distributed systems with Kafka and MQ. Work with Azure Data Lake Storage, troubleshoot application and cloud issues, participate in architecture and design reviews, and collaborate across engineering teams. The role may also include mentoring junior engineers and contributing to DevOps, security, performance, and continuous improvement initiatives.

Lead the architecture, design, and hands-on development of enterprise-scale cloud-native applications, data platforms, distributed systems, microservices, and event-driven solutions. Define technical strategy, establish engineering standards, conduct architecture reviews, and guide multiple engineering teams. Build solutions using Java, Scala, Azure, ADLS, Databricks, Kafka, and messaging platforms while mentoring engineers and driving DevOps, CI/CD, scalability, security, and operational excellence.

Conducts quality-control reviews of end-to-end AML investigations involving money laundering, sanctions, fraud, terrorist financing, bribery, and related risks. Reviews evidence, narratives, dispositions, and SAR recommendations; identifies investigative gaps and recurring quality issues; analyzes transactional and behavioral data; provides feedback; and supports improvements to AML procedures, controls, and regulatory compliance.

Develop machine learning and analytics solutions using large-scale datasets. Responsibilities include feature engineering, predictive modeling, workflow optimization, customer implementations, production deployments, stakeholder collaboration, technical training, and resolving data-related challenges. The role requires Python, PySpark, machine learning pipelines, MLflow or Airflow, statistical modeling, and strong communication skills, with preferred experience in fraud detection, risk analytics, compliance, cloud environments, and model monitoring.

Leads the development and execution of a global B2B marketing function focused on demand generation, pipeline growth, digital marketing, content, social media, partnerships, and brand awareness. Owns campaigns, ABM initiatives, marketing performance, budgets, reporting, and KPIs while collaborating with sales, product, customer success, and executives. This hands-on leader builds scalable programs, strategic alliances, and thought leadership initiatives that generate measurable revenue and market impact.

Deploys, customizes, integrates, and supports fraud risk scoring systems for financial institution clients. Responsibilities include software development, production deployment, troubleshooting, monitoring, system upgrades, Agile participation, and client-facing technical collaboration across cloud and on-premise environments.

Validate and optimize AML transaction monitoring and financial crime models. Responsibilities include threshold tuning, model performance assessment, data quality analysis, validation documentation, regulatory compliance, risk evaluation, and stakeholder reporting. The role collaborates with compliance, technology, data science, and business teams to improve detection effectiveness, reduce false positives, strengthen model governance, and support new monitoring scenarios.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

Develop and maintain enterprise BI solutions, dashboards, reports, data models, and ETL processes. Translate business requirements into scalable analytics solutions, define KPIs, analyze complex datasets, ensure data quality and governance, optimize performance, resolve reporting issues, establish BI standards, and mentor junior developers while partnering with technical and business stakeholders.

Analyze and tune sanctions, fraud, and AML models; evaluate performance, reduce false positives, develop features and datasets, automate analysis, troubleshoot data issues, and communicate findings to technical, compliance, regulatory, and senior management stakeholders.