EY
Consulting - Managed Services - AI-Native Software Engineer
Washington, District of Columbia
Sponsorship not specified$112k-$185kDetected 26 days ago
Full-Stack DevelopmentData StructuresCode ReviewAWSGCPAzureCI/CDDevOpsAPI DevelopmentGraphQLRESTRAGIncident ResponseAgileTest AutomationCollaborationMentoring
About the role
- Translate user stories and acceptance criteria into working software with strong engineering fundamentals (readability, modularity, performance).
Responsibilities
- You'll help build and modernize digital products and applications using an AI-first delivery approach.
- In this role, you'll pair traditional engineering fundamentals with AI tooling to accelerate design, coding, testing, documentation and release-while maintaining high quality, security and Responsible AI standards.
- AI-native delivery (build)
- Use AI coding assistants to generate, refactor and modernize code while applying secure-by-design practices and peer review discipline.
- Create and maintain reusable components, templates and accelerators that improve team velocity and consistency.
- Implement automated QA in CI/CD (linting, SAST/DAST, dependency scanning, performance checks, test gates).
- Support exploratory testing and defect triage; drive root-cause fixes rather than symptomatic patches.
- Produce clear technical documentation and runbooks (including AI-assisted documentation) to support supportability and knowledge transfer.
- Collaborate with product owners and designers to turn intent into implementable technical plans.
- The ability to work on transformative digital product builds with global clients.
Requirements
- Bachelor's degree in Computer Science, Engineering or a related discipline (or equivalent experience).
- Typically 4+ years of professional software development experience (consulting or product teams).
- Practical knowledge of automated testing and CI/CD
- ability to improve quality through automation.
- Experience with cloud-native development (Azure/AWS/GCP), containers and infrastructure-as-code concepts.
Nice to have
- Ideally, you'll also have
Skills
- Apply prompt patterns and structured context (docs, schemas, examples) to improve AI outputs and reduce rework.
- Instrument services for logs/metrics/traces; help define SLOs/SLIs and error budgets for critical paths.
- Assist with incident response by using AI tools to summarize telemetry, propose hypotheses and accelerate remediation-under human oversight.
- Communicate progress, risks and tradeoffs; contribute to estimation and sprint planning.
- Mentor junior teammates on AI-augmented engineering practices (prompting, validation, secure use of tools).
Compensation
- The base salary range for New York City Metro Area, Washington State and California (excluding Sacramento) is $134,700 to $210.700.
Benefits
- Career-long learning, certifications and coaching to grow both technical depth and consulting impact.
- Flexible work options (subject to engagement needs).
- We'll empower you in a flexible environment, and fuel you and your extraordinary talents in a diverse and inclusive culture of globally connected teams.
- We offer a comprehensive compensation and benefits package where you'll be rewarded based on your performance and recognized for the value you bring to the business.
- Individual salaries within those ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography.
- In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options.
- Under our flexible vacation policy, you'll decide how much vacation time you need based on your own personal circumstances.
- Curiosity, learning agility and the ability to explain technical concepts to non-technical stakeholders.
Equal opportunity
- EY is committed to providing reasonable accommodation to qualified individuals with disabilities including veterans with disabilities.
This listing is sourced directly from EY's careers page and normalized into a canonical job model.