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Provenir

AI-powered risk decisioning SaaS for finance

Quality Assurance Engineer

Test Automation EngineeringPosted on Nov 13
Full-timeBengaluru, Karnataka, IndiaNo salary listedRemote

About the Job

Requirements

  • A Bachelor's degree in Computer Science or a related field is required.
  • Proven extensive hands-on experience in automated testing of web applications and services, including designing, developing, and executing automated tests in Python using Playwright, Selenium WebDriver, or an equivalent modern browser automation framework.
  • Demonstrated understanding of maintainable end-to-end test architecture, such as the Page Object Model or equivalent patterns.
  • Extensive hands-on experience with Python scripting.
  • Experience testing web services and REST APIs using tools such as Postman or Swagger.
  • Working knowledge of graph databases such as Neo4j, Amazon Neptune, TigerGraph, or JanusGraph, including writing and validating graph queries.
  • Exposure to testing AI/ML-backed features and understanding why probabilistic systems require different assertions than deterministic systems.
  • Scripting experience beyond Python, such as Unix shell or Ruby.
  • Experience with continuous integration and pipeline-based delivery using Jenkins, GitHub Actions, GitLab CI, or similar tools.
  • Experience with SQL.
  • Experience recommending process improvements.

Responsibilities

  • Build and maintain end-to-end and component test suites using modern browser automation with auto-waiting, trace-based debugging, and parallel execution as the default.
  • Shape test coverage around the test pyramid, using API and contract tests as the primary safety net and reserving UI tests for genuine user journeys.
  • Embed quality gates in CI/CD pipelines, including sharded parallel runs, risk-based test selection, and pass/fail criteria that block promotion without manual sign-off.
  • Provision ephemeral, containerized test environments on demand through infrastructure-as-code, using synthetic and masked test data generated per run.
  • Track test flake rates, quarantine and fix unstable tests, and hold suites to explicit stability and runtime SLAs.
  • Use AI-assisted tooling for test authoring, coverage gap analysis, and locator resilience, while evaluating the generated tests.
  • Work shift-left with engineers by contributing test coverage alongside features and partnering on triage to drive issues to root cause.
  • Design and automate validation of graph database layers, including schema integrity, relationship correctness, and query behavior across Cypher, Gremlin, or SPARQL.
  • Build regression coverage for graph analytics outputs, including pathfinding, centrality, community detection, and link analysis used in fraud-ring identification and network risk scoring.
  • Validate graph ingestion and transformation pipelines for data completeness, deduplication, and referential accuracy at scale.
  • Develop evaluation harnesses for ML model behavior, including accuracy thresholds, drift detection, bias and fairness checks, and explainability outputs required for regulatory review.
  • Build automated test suites for agentic workflows, covering tool-calling correctness, multi-step orchestration, context handling, failure recovery, and guardrail enforcement.
  • Design assertion strategies for non-deterministic outputs, including golden-set comparison, semantic similarity scoring, and LLM-as-judge evaluation.
  • Establish prompt and model regression testing to catch behavioral changes before release.
  • Validate feature pipelines and training/serving consistency in partnership with data science teams.
  • Incorporate best practices into development, testing, and deployment processes and recommend improvements.
  • Support application onboarding to the defined target operating model to achieve build and release automation.
  • Document, track, and escalate issues as appropriate.

Desired Qualifications

  • Hands-on experience with agentic frameworks such as LangChain, LangGraph, Model Context Protocol, or comparable orchestration tooling.
  • Familiarity with LLM evaluation tooling and practices, including golden datasets, rubric-based scoring, hallucination and jailbreak testing, and red-teaming.
  • Experience with graph analytics libraries such as Neo4j GDS, Apache Spark GraphX, or NetworkX.
  • Experience with observability-driven testing, including synthetic monitoring, canary analysis, and validation through traces, logs, and metrics.
  • Experience with Docker, Kubernetes, Terraform, and Ansible, as well as microservices, container deployment, and service orchestration.
  • Experience with cloud APIs such as AWS, Azure, or GCP.
  • Experience with JIRA for issue tracking and escalation.

About the Company

Provenir

AI-powered risk decisioning SaaS for finance

Provenir provides a cloud-native, AI-powered risk decisioning platform for the financial services industry. It offers a SaaS platform that integrates data, AI, and decisioning into one system to support the full lifecycle of risk-related decisions. Core product features include a low-code/no-code interface that lets business users design, test, and deploy risk strategies without heavy IT, and data orchestration through a marketplace of over 530 data and partner integrations. The platform covers identity verification, fraud detection, credit risk assessment, loan origination, customer onboarding, and collections, and handles billions of transactions annually. Provenir serves banks, fintechs, BNPL providers, challenger banks, and other financial institutions, with a global footprint across nearly 50 countries. Its goal is to help customers make faster, smarter, and compliant risk decisions by providing an end-to-end decisioning engine in a scalable SaaS solution.

Company size
201-500
Company stage
Not listed
Total funding
Not listed
Headquarters
Parsippany-Troy Hills, NJ
Founded
2004

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