Archived listing. The details below describe a past opening.
Required Skills & Experience
A Lead SDE role grounded in strong software engineering fundamentals, with the judgment and practical fluency to use AI effectively, responsibly, and safely across product development and the software delivery lifecycle
- Strong experience designing, developing, testing, delivering, and operating production software products or platforms.
- Strong foundation in software architecture, object-oriented or functional design, APIs, distributed systems, data stores, testing, secure development, and operational excellence.
- Hands-on proficiency in one or more modern programming languages and frameworks. Experience with Java, Spring Boot, Python, TypeScript, React, or comparable technologies is relevant.
- Experience building cloud-based solutions using AWS, Azure, or comparable platforms and working with modern development, deployment, and observability practices.
- Ability to lead technical design discussions, compare approaches, document decisions, and guide implementation across multiple components or services.
- Demonstrated ability to use AI-assisted tools in software engineering work and to validate their output with appropriate technical rigor.
- Working knowledge of modern AI concepts and application patterns sufficient to identify opportunities, understand limitations, and collaborate with specialists when deeper AI expertise is required.
- Demonstrates effective use of AI-assisted development tools by appropriately delegating tasks, providing clear context and requirements, optimizing prompt efficiency, and understanding AI capabilities and limitations to maximize productivity.
- Applies strong engineering judgment to validate AI-generated outputs, identify hallucinations, mitigate bias and errors, protect sensitive information, and maintain ownership of technical decisions, quality, security, and outcomes.
- Ability to translate ambiguous business or product needs into practical technical solutions and iterate using evidence and feedback.
- Strong written and verbal communication skills, with the ability to build alignment across technical and non-technical stakeholders.
- Ability to work independently, mentor others, surface risks early, and deliver high-quality outcomes in a rapidly changing R&D environment.
Nice to Have Skills & Experience
- Experience prototyping or delivering software products that integrate generative AI, machine learning services, retrieval, intelligent search, natural-language interfaces, or agent-enabled workflows.
- Experience with enterprise data platforms or streaming and analytics technologies such as Databricks, Snowflake, Spark, Kafka, Postgres, or similar technologies.
- Experience developing evaluation, testing, monitoring, or fallback approaches for AI-enabled application behavior.
- Experience introducing emerging technologies or new engineering practices into a team while balancing experimentation with security, compliance, maintainability, and production readiness.
- Experience working in payments, financial services, commerce, or another highly regulated environment.
Job Description
We are looking for a Lead Software Development Engineer with strong software engineering fundamentals and AI fluency. You will design, build, test, and deliver scalable products and platform capabilities in a fast-moving R&D environment. You will apply sound engineering judgment, guide technical decisions, and use AI-enabled tools and capabilities where they improve speed, quality, learning, or customer value. This is a software engineering role, not a model-research or data-science role. Success requires deep ownership of architecture, implementation, security, reliability, and operational outcomes, together with the ability to collaborate with AI effectively and critically.
- Own the design and delivery of complex software capabilities from early technical discovery and prototyping through production readiness and operation.
- Translate customer, product, and business intent into clear technical approaches, implementation plans, interfaces, and measurable outcomes.
- Design and build secure, scalable, maintainable applications, APIs, services, data pipelines, and user experiences across cloud and enterprise environments.
- Make and communicate architecture decisions, technical trade-offs, and risk assessments across services, systems, and teams.
- Rapidly prototype and validate emerging technologies while establishing a credible path from experiment to production.
- Use AI-assisted engineering tools to support activities such as discovery, design exploration, coding, testing, debugging, documentation, and code review, while remaining accountable for the resulting work.
- Create clear specifications and sufficient context so that both people and AI tools can contribute effectively to engineering tasks.
- Critically review AI-generated code, tests, explanations, and recommendations for correctness, security, maintainability, architectural fit, and business intent.
- Identify where AI-enabled capabilities can improve a product or workflow, and recognize when conventional software approaches are more appropriate.
- Apply responsible AI, data protection, intellectual property, security, and enterprise governance expectations when using or integrating AI capabilities.
- Establish appropriate validation, testing, observability, and human review for software that incorporates probabilistic or AI-enabled behavior.
- Drive engineering quality through code reviews, automated testing, CI/CD, production-readiness practices, troubleshooting, root-cause analysis, and operational improvement.
- Partner effectively with product, design, data, architecture, security, and engineering stakeholders to build alignment and resolve cross-functional dependencies.
- Mentor engineers, share reusable practices, and help the team strengthen both core engineering capability and practical AI fluency.