You will be responsible for designing, developing, and maintaining scalable Python and AI applications while providing technical leadership and mentorship to the team. Additionally, you will collaborate with clients to analyze requirements and integrate multi-modal GenAI models into production systems.
Design, build, and optimize ETL/ELT pipelines that turn customer and internal data into actionable datasets, and partner with Markets and Product teams on reporting tools and dashboards. Build and maintain data lakes and feature stores that support the ML Engineering team’s machine learning solutions for subrogation.
Develop and execute automated test scripts using the Calypso Automated Testing Tool to ensure high-quality front-to-back workflows. Coordinate with technical teams to manage test environments, validate data, and track defects throughout the software development lifecycle.
The engineer will provide architectural oversight, lead technical guild activities, and ensure high-quality software delivery through code reviews and unit testing. They will also collaborate across teams to define functional requirements and mentor other engineers to improve long-term maintainability.
You will own the quality strategy for the project, designing and maintaining CI/CD pipelines and automated test frameworks using TypeScript and Playwright. Additionally, you will lead root cause analysis, standardize QA processes, and mentor junior team members while implementing AI-based solutions.
Design, build, and maintain core platform components including CI/CD, observability, and multi-account AWS landing zones. Lead the evolution of platform abstraction layers and provide architectural guidance to engineering teams.
Collaborate with stakeholders to translate complex business processes into structured, executable instructions for AI agents. Design, implement, and maintain reusable Claude Code skills while providing technical leadership and defining platform-level standards.
You will be responsible for hands-on development, automation, and improving the performance, scalability, and reliability of applications. Additionally, you will manage database optimization, containerized environments, and CI/CD pipelines within an AWS stack.
Analyze marketing workflows to design and integrate scalable AI-enabled business solutions and Generative AI tools into the MarTech ecosystem. Collaborate with cross-functional engineering and security teams to ensure AI services meet enterprise standards for scalability, governance, and performance.
Design and develop scalable backend applications and microservices using Java, Spring Boot, and Quarkus. Implement event-driven solutions on AWS and enhance observability practices within an Agile environment.
Assess and document the current Martech ecosystem to identify redundancies and design a simplified, scalable target-state architecture. Collaborate with cross-functional stakeholders to translate business requirements into practical architecture decisions and implementation plans for AI-driven initiatives.
Build and maintain core platform components including SSO, networking, and CI/CD pipelines while managing AWS landing zones and container orchestration. Design platform 'golden paths' and provide self-service tools to enable fast, secure deployments for distributed teams.
Assess and document the current Martech ecosystem to identify redundancies and design a simplified, scalable target-state architecture. Collaborate with cross-functional stakeholders to translate business requirements into practical architecture decisions and implementation plans.
Design and maintain enterprise-grade Java applications and customize Murex components for Capital Markets platforms. Develop real-time and batch integrations while leveraging AI-assisted tools to optimize productivity and code quality.
Act as a liaison between front-office users and technical teams to gather and analyze business requirements for Murex modules. Support the implementation, configuration, and optimization of trading workflows and financial product structures.
Design and evolve cloud-native, service-oriented architectures on Microsoft Azure, integrating AI and Generative AI capabilities. Provide technical leadership on cloud modernization, legacy migration, and the implementation of scalable architectural patterns.
Design, build, and optimize ETL/ELT pipelines to transform data into actionable datasets for business strategy. Maintain data lakes and feature stores to support the ML Engineering team in developing subrogation solutions.
