Help turn cutting edge AI research into real, production grade capabilities used across the firm. You will build secure, scalable platforms and applications that accelerate experimentation while meeting high engineering standards. You will partner closely with AI Researchers and Data Scientists to move prototypes into optimized, reliable solutions. You will grow your influence by mentoring others and helping shape team practices. You will join a team that values collaboration, inclusion, and continuous improvement.
As a Software Engineer III in AI and Machine Learning Data Platforms, you will deliver critical technology solutions that support the firm’s business objectives. You will work directly with AI Research and the Machine Learning Center of Excellence to advance experiments into robust, scalable applications. You will build platform and application capabilities, including generative AI solutions, with a focus on security, stability, and operational excellence. You will contribute to an engineering culture that values strong delivery practices, mentorship, and inclusive teamwork.
Job Responsibilities
- Collaborate with Data Scientists and AI Researchers to advance experiments into scalable, optimized, production grade applications
- Develop software applications for AI and machine learning platforms, including generative AI applications such as agents
- Design and troubleshoot technical solutions using creative problem solving to address complex engineering challenges
- Identify and automate remediation for recurring issues and developer pain points to improve operational efficiency
- Deliver solutions using continuous integration and continuous delivery pipelines to public and private cloud platforms
- Support experimentation environments, including tools such as Jupyter Notebooks
- Mentor junior engineers and help drive engineering practices across the team and with research partners
- Contribute to a team culture of diversity, opportunity, inclusion, and respect
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Required Qualifications, Capabilities, and Skills
- Formal training or certification in software engineering concepts with applied full stack development experience
- Practical experience developing infrastructure as code, ideally using Terraform
- Hands-on experience building applications, testing, and supporting operational stability
- Proficiency in at least one programming language, with a strong focus on Python
- Experience with automation and continuous integration, delivery, and testing methods
- Working knowledge of the Software Development Life Cycle and Model Development Life Cycle
- Understanding of agile methodologies and basic proficiency with architectural frameworks
- Demonstrated experience in platform development within a technical discipline such as cloud, artificial intelligence, or machine learning
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
Preferred Qualifications, Capabilities, and Skills
- Experience using AI tools to improve productivity and efficiency in daily work
- Proactive approach to identifying issues and challenging the status quo constructively
- Initiative in learning and adapting to new technologies and methodologies
- Experience or exposure to business facing integrated application environments such as risk or trading
- Strong problem solving skills with a focus on innovation and continuous improvement
- Strong communication and collaboration skills across cross functional teams