AI QE/SDET Engineer

Capgemini•Los Angeles, CA

3d•Hybrid

About The Position

This role sits within a client's spec-driven SDLC platform — an agent-first system where the specification is the single source of truth and downstream artifacts are generated from it, not described by it. The platform spans Requirements authoring, Test Data Management, CodeGen, and the QE test harness on a common foundation, built on three pillars: agent-first, input-flexible, and deterministic. Deployed on ROSA with Snowflake as the data platform and Portkey as the AI gateway.

Requirements

Experienced software engineering, recent platform/backend work
Demonstrated experience re-platforming an IDE-bound tool into a shared service — core to the role
Strong Python and/or TypeScript; API design, event-driven orchestration
Test automation depth — Gherkin/BDD, Playwright or Selenium, CI integration
Agentic/LLM frameworks (LangGraph, CrewAI, Strands) with deterministic control around probabilistic components
Kubernetes/OpenShift (ROSA a plus), Snowflake, AI gateway patterns
LLM cost-awareness — token economics as a design constraint

Nice To Haves

TDM or synthetic data pipeline ownership
Brownfield sequencing experience
Insurance domain experience

Responsibilities

Re-architect the harness for headless, multi-tenant execution — durable orchestration, queued and scheduled runs
Wire it into the E2E SDLC spine.
Integrate with TDM's two-lane architecture and the Repo Knowledge Plane
Instrument telemetry, flake detection, and stability scoring that gate promotion