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AI Summary
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.
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