AI Test Engineer - Senior Manager

About Vialto Labs (VLabs) 

 

Vialto Labs (VLabs) is responsible for redesigning how work is delivered in the tax and immigration service lines, as well as driving operational efficiency across Vialto’s functional areas using AI. The team builds and deploys novel AI-enabled solutions that directly improve productivity and increase delivery quality for our clients. VLabs is accountable for rapidly turning innovative experiments into production-ready deliverables at scale and embedding them into day-to-day operations. This team focuses on the highest-impact workflows, creating standardized, repeatable capabilities that can be deployed globally. Operating with a mandate for speed and measurable outcomes, VLabs works alongside service line, product, and platform leaders. 

 

About the Role 

 

The Senior Manager, AI Test Engineering is a hands-on role within VLabs Quality Engineering, responsible for validating the performance, reliability, and integrity of AI-enabled solutions in production environments. This role operates at the intersection of AI engineering and quality assurance, ensuring that outputs from LLMs, OCR pipelines, document classification models, and agentic workflows perform as expected at scale and meet defined business performance thresholds.  Working closely with the Programme Test Manager and partnering with engineering, product, and delivery teams, this role translates AI testing strategy into executable frameworks, evaluation pipelines, and reusable assets embedded into the delivery lifecycle. 

 

Success requires independent execution, strong technical depth, and the ability to proactively identify risks, patterns, and performance gaps while enabling rapid, production-grade deployment of AI capabilities. 

 

Key Responsibilities 

 

AI Evaluation & Test Design 

Translate AI testing strategy into executable test scenarios across LLM outputs, document classification, extraction accuracy, agent workflows, and edge cases 

Design adversarial and boundary test inputs to expose hallucination, misclassification, and failure modes 

Validate AI outputs for structure, consistency, accuracy, and production readiness against defined performance thresholds 

 

Evaluation Engineering & Automation 

 

Build reusable Python-based evaluation frameworks, including output validation, hallucination detection, and scoring mechanisms 

Develop parameterized test scripts reusable across features, models, and releases 

Implement AI-as-Judge frameworks, including prompt design, scoring logic, and calibration of evaluation reliability 

Embed evaluation frameworks into CI/CD pipelines to support continuous testing and deployment 

 

Drift Detection & Quality Monitoring 

Design and operate drift detection frameworks using fixed baseline datasets and scheduled re-evaluation 

Establish thresholds to distinguish acceptable variation from performance degradation 

Enable release gating by identifying regressions prior to production deployment 

 

Ground Truth & Data Quality 

Build and maintain ground truth datasets in partnership with subject matter experts 

Define standards for classification, extraction accuracy, and acceptable output characteristics 

Continuously update datasets to reflect evolving business requirements and use cases 

 

Workflow & Integration Testing 

Test end-to-end agentic workflows, validating data integrity, error propagation, and fallback behavior 

Perform API-level testing of AI pipeline endpoints using Python and Postman/Newman 

Validate data persistence and integrity across system layers using SQL 

Partner with engineering teams to ensure testability, observability, and system reliability 

 

Standardization & Scaling 

Define and scale standardized AI evaluation patterns and reusable quality frameworks across VLabs 

Contribute to enterprise AI quality standards and reference architectures 

 

Governance & Responsible AI 

Ensure adherence to Responsible AI, data privacy, and governance requirements 

Support auditability, traceability, and transparency of AI outputs and evaluation processes 

 

Stakeholder Enablement 

Translate evaluation results into actionable insights for engineering, product, and business stakeholders 

Support decision-making on model readiness, release risk, and performance trade-offs 

Proactively identify risks, patterns, and systemic issues and escalate appropriately 

 

Qualifications & Experience 

 

Professional Experience 

7+ years in software testing, including 2–3 years focused on AI/ML-enabled systems in production environments 

Proven experience designing and executing AI evaluation frameworks and quality strategies 

Strong track record building ground truth datasets, drift detection systems, and scalable evaluation pipelines 

Experience testing multi-step agentic workflows and AI-driven automation systems 

Experience operating in fast-paced, iterative delivery environments 

Background in regulated or compliance-driven environments preferred 

 

Technical Expertise 

Advanced Python programming for evaluation frameworks, batch processing, and data analysis 

Experience with LLM evaluation tools such as deepeval, RAGAS, promptfoo, or similar 

Strong capabilities in: 

AI output validation, hallucination detection, and grounding checks 

Drift detection frameworks and statistical evaluation methods 

OCR, VLM, and document AI testing (classification, extraction, edge cases) 

API testing using Python (requests/httpx) and Postman/Newman 

SQL for data validation and pipeline integrity checks 

Familiarity with LangChain, LlamaIndex, or similar frameworks 

Experience with cloud AI platforms such as Azure AI Foundry or AWS Bedrock preferred 

 

Operating Capabilities 

Ability to operate independently in fast-moving, ambiguous environments 

Strong analytical mindset with attention to detail and quality rigor 

Ability to balance speed and rigor in AI evaluation and delivery cycles 

Proactive communicator who identifies risks and drives resolution 

Ability to translate technical findings into business-relevant insights 

 

Education 

Bachelor’s degree required; Advanced degree in Computer Science, Data Science, or related field preferred 

We are an equal opportunity employer that does not discriminate on the basis of any legally protected status.
Please note, AI is used as part of the application process.