Senior Associate Software & Platform Engineer

LSEG•New York, NY

7d•$100,200 - $167,000•Hybrid

About The Position

We are looking for a Senior Associate Software & Platform Engineer to join our FX Engineering group as an individual contributor. This role is suited for an engineer who has strong foundational experience in Java application development, Spring Boot, build systems, testing practices, CI/CD, and cloud-based platform engineering. The successful candidate will contribute to the development, testing, deployment, and support of microservices and platform components used within a large-scale trading technology environment. This role will work closely with senior engineers, technical leads, DevOps/SRE teams, architects to deliver secure, reliable, and maintainable software solutions.

Requirements

Hands-on experience with Java development.
Experience building applications or services using Spring Boot.
Experience with Java build tools such as Maven or Gradle.
Strong understanding of software development fundamentals, including clean coding, debugging, source control, and code reviews.
Experience writing and maintaining unit tests and integration tests.
Knowledge of CI/CD concepts and experience working with CI/CD platforms such as GitLab CI/CD, GitHub Actions, Jenkins, Azure DevOps, or similar tools.
Experience with at least one public cloud platform such as Azure, AWS, or GCP.
Familiarity with microservices concepts, REST APIs, application configuration, logging, and service deployment.
Basic understanding of containerization and cloud-native application practices.
Ability to troubleshoot technical issues using logs, metrics, build output, test results, and application behavior.
Good understanding of Agile software delivery practices.
Strong communication skills and ability to collaborate effectively with globally distributed teams.
Ability to work as an individual contributor while taking ownership of assigned development, testing, and support tasks.

Nice To Haves

Exposure to Kubernetes, Docker, Helm, or container-based deployment platforms.
Experience with observability or monitoring tools such as DataDog, Grafana, Prometheus, Splunk, ELK/OpenSearch, or similar platforms.
Exposure to secure software development practices, secrets management, vulnerability scanning, or cloud security controls.
Python development experience, including designing and implementing reusable Python packages, shared libraries, automation utilities, or internal developer tools.
Experience with agent development, including building, configuring, testing, and deploying AI agents or workflow automation agents.
Exposure to deploying and operating custom AI/ML models in cloud environments, including model packaging, runtime configuration, monitoring, and integration with application workflows.
Understanding of agentic development principles and best practices, including prompt design, tool/function calling, guardrails, observability, evaluation, testing, versioning, and responsible use of AI-enabled automation.
Experience using AI-assisted tools to improve developer productivity, generate tests, analyze code, automate operational workflows, accelerate incident diagnosis, or enhance observability.
Engineers who think critically, ask thoughtful questions, challenge assumptions respectfully, and use data to understand problems before proposing solutions.
Experience using data-driven insights and AI-assisted tools to improve observability, automate operational workflows, accelerate incident diagnosis, generate tests, support code analysis, or improve developer productivity is highly valued.
Curiosity, a learning mindset, and the practical application of AI-enabled engineering practices are most important.

Responsibilities

Develop and maintain Java-based applications and microservices using Spring Boot.
Build clean, efficient, testable, and maintainable code aligned with engineering standards.
Work with build systems such as Maven or Gradle to compile, package, test, and manage application dependencies.
Create and maintain unit tests and integration tests to ensure software quality and reduce production risk.
Support application build, deployment, and release activities using CI/CD platforms.
Contribute to automation of development, testing, deployment, and operational workflows.
Work with public cloud platforms such as Azure, AWS, or GCP to support application deployment and runtime operations.
Assist with troubleshooting application issues across code, configuration, build, deployment, and cloud environments.
Collaborate with senior engineers and platform teams to improve reliability, observability, and operational readiness.
Participate in Agile ceremonies, sprint planning, backlog refinement, and regular engineering discussions.
Contribute to technical documentation, runbooks, troubleshooting guides, and knowledge-base updates.
Learn and apply DevOps/SRE principles including automation, monitoring, incident awareness, and production support practices.
Support continuous improvement across code quality, testing coverage, deployment reliability, and developer productivity.
Use AI-assisted engineering tools where appropriate to help with code analysis, test generation, documentation, troubleshooting, and workflow automation.