Software Engineer

Our client is currently seeking a Software Engineer Location : Hybrid in Austin, TX (4 days a week onsite)
Duration : 3 months with possibility of extension


Minimum qualifications:

• Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience.
• 5 years of experience developing enterprise applications using Java, JavaScript, and modern web frameworks (e.g., Angular).
• Experience building and consuming REST APIs, working with service-based architectures, and implementing gateway patterns (e.g., Spring Boot, Spring Cloud Gateway).
• Experience with automated testing frameworks (e.g., JUnit, Karma, Cucumber, BDD) and database platforms (e.g., MongoDB, PostgreSQL).
• Experience with CI/CD technologies, code quality tooling, and observability/log analysis platforms (e.g., Splunk, BigQuery, Grafana).
• Understanding of modern authentication concepts, including OAuth, SAML, and JWT.
• Practical experience using Generative AI coding assistants (e.g., GitHub Copilot, Claude) within IDE, CLI, or developer workflow environments.
• Experience using agentic workflows, spec-driven development, structured prompting, and custom instructions for AI-assisted software delivery.

Preferred qualifications:

• Master's degree in Computer Science, Engineering, or a related field.
• Experience designing or implementing modern authentication journeys, including Passwordless Login, FIDO2/WebAuthn/passkeys, or Risk-Based Authentication (risk scoring, step-up authentication, behavioral signals).
• Experience deploying containerized applications to cloud platforms (e.g., GCP, AWS).
• Experience with enterprise CI/CD and code security tooling (e.g., GitHub Actions, Bamboo, Sonar, Blackduck, Veracode).
• Familiarity with NIST identity standards and developing software for highly regulated or security-sensitive enterprise environments.
• Experience with performance testing and performance engineering.
• Proven ability to apply AI-assisted development practices to improve delivery velocity, test coverage, troubleshooting efficiency, and overall developer productivity.

About the job:

As a Software Engineer on the Enterprise Middleware and Online Security Technology team, you will serve as a highly independent contributor owning complex features, driving critical design decisions, and delivering secure, scalable authentication capabilities. You will help build and support enterprise web applications and modern authentication journeys, focusing heavily on Passwordless Login and Risk-Based Authentication. This role requires strong engineering judgment, disciplined production support, and the ability to partner seamlessly with product, security, fraud, risk, and platform architecture stakeholders. A core component of this role is the practical application of AI-assisted engineering workflows across the entire software development lifecycle. You will leverage Generative AI coding assistants, structured prompts, validation practices, and emerging agentic development patterns to continuously improve delivery efficiency, code quality, and documentation while maintaining strict engineering ownership.

Responsibilities:

• Own the design, development, testing, and support for complex application components and secure authentication capabilities.
• Partner with product owners, SCRUM masters, architects, and engineers to decompose features, estimate delivery efforts, and meet strict sprint and release commitments.
• Design and implement secure, resilient, user-friendly authentication journeys utilizing risk signals, telemetry, and policy-based decisioning.
• Build and consume REST APIs, gateway patterns, and reusable services that align with platform principles and architectural standards.
• Maintain rigorous standards for automated testing, CI/CD pipelines, code quality, security scanning, observability, and overall release readiness.
• Troubleshoot high-stress, time-critical production situations with strong ownership, and mentor less-experienced engineers through code reviews and design discussions.
• Utilize AI-assisted engineering tools for daily SDLC activities, including implementation, refactoring, unit testing, regression support, code review preparation, and documentation.
• Apply agentic and spec-driven development practices by translating design inputs (API contracts, data models, integration details) into structured prompts that guide AI-assisted implementation.
• Validate all AI-generated recommendations, code, tests, and documentation using secure coding practices and established team quality standards, sharing prompt strategies and learnings with peers.