Staff Software Engineer, AI/ML

17 days ago

Hyderābād, IndiaStaff+

Responsibilities

Build and ship production software including services, APIs, data models, user interfaces, integrations, tests, and operational tooling.
Direct AI coding agents through specifications, acceptance criteria, implementation plans, and verification steps.
Review and own AI-generated code, tests, pull requests, and migrations for correctness, security, maintainability, and production readiness.
Establish repeatable AI-assisted engineering practices, tooling workflows, context patterns, automated verification, and reusable delivery templates.
Design and implement integration-heavy, data-intensive, and workflow-driven enterprise systems.
Track delivery metrics including cycle time, quality, rework, test coverage, cost, adoption, and operational health.
Improve testing, review, observability, documentation, and operational practices for AI-assisted delivery.
Partner with Product, Security, business stakeholders, and Enterprise Technology teams, while mentoring engineers and influencing technical decisions.

12+ years of experience building and shipping production software as a hands-on engineer, senior individual contributor, technical lead, or Staff-level contributor.
Strong production experience with Python and/or TypeScript across backend, frontend, data, API, or cloud-native systems.
Hands-on experience using AI coding agents or tools such as Claude Code, Google Antigravity, Codex, Cursor, or similar systems.
Strong judgment in reviewing AI-generated code and determining where human oversight is required.
Experience building or integrating services, APIs, relational data stores, event-driven systems, and user-facing workflows.
Familiarity with cloud-native development, preferably on GCP, including monitoring, security scanning, and cost-aware engineering.
Understanding of secure engineering practices, role-based access, SSO, audit logging, and enterprise controls.
Ability to influence technical decisions, mentor engineers, and raise quality across a significant system or project area.
Clear written and verbal communication with engineering and non-engineering stakeholders.
Preferred experience includes enterprise systems, business applications, workflow platforms, data platforms, internal tools, media or streaming domains, developer-facing tools, internal platforms, LLM or agent observability, data migration, compliance-sensitive systems, SOX controls, audit-heavy environments, or complex enterprise integrations.

1,001-5,000 employees