Job Description

The opportunity

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. You’ll work in cross-functional squads (product, design, engineering, QA, DevOps) delivering outcomes for clients across industries.

Your key responsibilities

AI-native delivery (build)

• Use AI coding assistants to generate, refactor and modernize code while applying secure-by-design practices and peer review discipline.

• Translate user stories and acceptance criteria into working software with strong engineering fundamentals (readability, modularity, performance).

• Create and maintain reusable components, templates and accelerators that improve team velocity and consistency.

• Apply prompt patterns and structured context (docs, schemas, examples) to improve AI outputs and reduce rework.

Quality engineering (test)

• Use AI tools to draft unit/integration tests, contract tests and test data; validate coverage and edge cases with engineering judgment.

• 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.

DevSecOps and observability (run)

• Contribute to pipelines, environments and release automation; ensure builds are repeatable and auditable.

• 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.

Client and team collaboration

• Collaborate with product owners and designers to turn intent into implementable technical plans.

• 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).

Skills and attributes for success

• Strong foundation in software engineering: data structures, APIs, version control, code review and debugging.

• Hands-on experience building web and/or cloud applications (front-end, back-end or full-stack).

• Practical knowledge of automated testing and CI/CD; ability to improve quality through automation.

• Comfort using AI tools (coding assistants, test generation, documentation) with disciplined validation and security awareness.

• Curiosity, learning agility and the ability to explain technical concepts to non-technical stakeholders.

Ideally, you’ll also have

• Experience with cloud-native development (Azure/AWS/GCP), containers and infrastructure-as-code concepts.

• Familiarity with modern front-end frameworks and/or API design patterns (REST/GraphQL/event-driven).

• Exposure to Responsible AI concepts (privacy, data handling, bias, model risk) and secure coding standards.

• Experience building with or integrating GenAI capabilities (prompting, RAG patterns, embeddings, evals) is a plus.

Qualifications

• 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).

• Experience delivering in Agile teams and collaborating across product, design and QA.

What we look for

We’re looking for people who are builders and problem solvers—team-oriented, client-focused and committed to quality. You bring a ‘learn fast, ship responsibly’ mindset, combine AI acceleration with sound engineering judgment, and help teams adopt new ways of working without compromising trust.

What we offer you

• The ability to work on transformative digital product builds with global clients.

• A collaborative, inclusive culture with access to modern engineering platforms and AI-enabled delivery tools.

• Career-long learning, certifications and coaching to grow both technical depth and consulting impact.

• Flexible work options (subject to engagement needs).

Responsible use of AI tools (non-negotiable)

• Follow EY and client policies for data handling; do not enter confidential client data into non-approved tools.

• Validate AI-generated outputs through testing, review and security checks; humans remain accountable for final deliverables.

• Use AI to accelerate work—not to bypass controls, quality gates or professional judgment.

EY | Building a better working world