Lead AI Engineer Agentic Test Automation

Schedule: Standard
Shortlisting Deadline: July 23rd

Interview Information:Rounds: 2 rounds

Duration: 30-60 mins

Interview Type: 1

1st round virtual | 2nd round onsite

Interview Placeholders: Targeting Jul 28th Aug 4th

Interview Debrief: TBD Will be scheduled with suppliers once all interview rounds are completed.

JOB DESCRIPTION

1) Agentic test automation foundation (reusable patterns + reference implementations)

Design and implement agentic testing patterns that can be adopted by multiple Underwriting teams (and later other domains).

Create reference implementations (sample repos / templates) demonstrating:

o Test generation assistance (from requirements, APIs, contracts, schemas)

o Test maintenance assistance (auto-updating selectors/contracts, flaky test triage)

o Failure analysis assistance (root cause suggestions, log correlation, defect drafting)

Establish a standard architecture for test code organization, tagging, data management, and execution across UI + API + service layers.

2) Coverage standards, templates, and governance

Define and publish coverage standards (what good looks like) including:

o Minimum coverage expectations by service/component

o Test type mix (unit vs API vs UI vs contract vs integration)

o Risk-based prioritization and traceability to requirements

Provide templates usable across teams:

o Test plan templates

o Test case/spec templates (Gherkin-style or equivalent)

o Definition of Ready / Definition of Done quality checklists

Create a scalable tagging/metadata strategy (e.g., feature, service, risk, priority, data

sensitivity) to support reporting and quality gates.

3) GenAI-assisted reporting and quality insights across microservices

Build automated reporting that aggregates test + service data across multiple microservices, such as:

o Test execution results (Karate/Playwright + CI runs)

o Service health signals (logs/metrics/traces if available)

o Defect signals (issue tracker metadata if available)

Generate GenAI-driven summaries:

o Release readiness narratives

o Failure clustering and trend analysis

o What changed? insights (commit/PR correlation)

Produce outputs consumable by engineering leadership and teams (dashboards,

markdown summaries in PRs, artifacts in CI).

4) Quality gates via agents

Build automated review agents that evaluate user stories/requirements for minimum

required clarity and data before development/testing starts:

o Required fields present (acceptance criteria, testable outcomes, data needs,

dependencies)

o Ambiguity detection and missing edge cases

o Data/privacy considerations and environment needs

Integrate gates into workflow (PR checks, issue templates, GitHub Actions) to reduce churn

and rework.

Required Technical Skills (must-have)

GenAI / LLM + agentic development

Hands-on experience building LLM-powered agents (tool-using, multi-step reasoning,

guardrails).

Experience with prompting patterns, structured outputs (JSON schemas), evaluation, and reducing hallucinations.

Ability to design agent workflows for:

o Test generation/augmentation

o Requirements review and completeness validation

o Report generation and summarization

GitHub platform + GHCP (Copilot) for engineering workflows

Strong proficiency with GitHub Copilot in day-to-day development.

Deep experience with GitHub platform capabilities:

o GitHub Actions (CI/CD pipelines, reusable workflows, composite actions)

o PR checks, branch protections, CODEOWNERS, templates

o Automation via GitHub APIs/webhooks (as needed)

Test automation engineering (framework expertise)

Advanced experience designing and implementing automation with:

o Karate (API testing, contract-like checks, data-driven testing, mocks)

o Playwright (UI automation, selectors strategy, parallelization, trace/video artifacts)

Strong understanding of test design and coverage:

o Happy path scenarios

o Negative/validation scenarios

o Edge/boundary scenarios

o Data setup/teardown strategies and test isolation

Cross-service reporting and data aggregation

Proven ability to aggregate and normalize results from multiple microservices and multiple pipelines.

Experience producing actionable automated reports (trend analysis, failure clustering, service correlation).

Automated requirements review agents

Experience implementing automated checks that validate:

o Acceptance criteria completeness

o Required test data and environment dependencies

o Non-functional requirements (performance, security, observability) when applicable

Deliverables / What success looks like (for the posting)

A reusable agentic testing automation kit adopted by multiple teams.

Published coverage standards + templates and onboarding documentation.

A working GenAI-assisted reporting pipeline aggregating results across microservices.

Automated quality gates integrated into GitHub workflows that measurably reduce story churn.