V&V Engineer for Medical Domain

Job Requirements
Role Summary
V&V Engineer
Key Responsibilities
Derive test conditions from requirements and maintain end-to-end traceability from requirement to execution, defect, evidence, and closure.
Create and execute test strategies, plans, protocols, automation frameworks, and comprehensive V&V activities across functional and non-functional areas.
Drive quality throughout the SDLC, collaborating with development, product, architecture, and quality teams.
Build and maintain automation frameworks, test infrastructure, cloud environments, CI/CD pipelines, and quality metrics.
Validate AI-enabled products and services using approved tooling, evaluation frameworks, security testing, and governance controls.
Perform performance, resilience, security, scalability, cloud, hybrid, and edge deployment validation.
Produce objective evidence, V&V reports, release readiness assessments, and support audits and reviews.
Mentor junior engineers and contribute to quality engineering best practices and standards.
Work Experience
Required Qualifications and Technical Skills
Experience & Education
Bachelor's degree in Computer Science, Engineering, or other STEM discipline.
8–10+ years of professional software engineering experience, including 5–7+ years in software verification, validation, quality engineering, and test automation.
Experience operating within regulated or quality-controlled environments requiring requirements traceability, documented evidence, approved protocols, and controlled test execution.
Core Technical Skills
Strong automation engineering expertise using Selenium, JMeter, LoadRunner, Silk Test, UFT/QTP, TestComplete, Watir, Cucumber, or similar tools.
Hands-on programming experience with Python, Java, C++, or C# and development of API/UI automation frameworks.
Expertise in automation framework design, maintenance, extension, migration, and optimization.
Strong knowledge of SDLC, Agile/Lean/XP methodologies, CI/CD practices, software security, testing techniques, data structures, and algorithms.
Experience across unit, integration, system, functional, regression, exploratory, non-functional, performance, security, scalability, installability, migration, upgrade, and acceptance testing.
Linux/Windows scripting (Bash, PowerShell), networking fundamentals (TCP/IP, DNS, routing, firewalls, proxies, load balancing, packet analysis), and VMware administration-level expertise.
Experience validating Data Center, virtualized, hybrid, and cloud-hosted solutions.
AI-Enabled Quality Engineering
Practical use of AI coding assistants (GitHub Copilot, Claude, Cursor, or equivalent) to generate, refactor, and maintain automation frameworks while applying engineering judgment and code review.
Ability to generate deterministic, traceable, and verifiable test cases from requirements, user stories, specifications, APIs, and data models using AI.
Experience with AI-driven test design, coverage analysis, predictive test selection, flaky-test detection, automated triage, failure clustering, and regression optimization.
Hands-on knowledge of AI-enabled testing platforms, self-healing automation, visual AI validation, agentic testing, and associated risks.
Experience validating AI/ML systems including evaluation harnesses, statistical acceptance criteria, model regression testing, RAG validation, hallucination detection, guardrail verification, prompt injection, jailbreak testing, and OWASP LLM security practices.
Understanding of AI governance, evidential integrity, data privacy, tool validation requirements, and responsible use of AI in regulated environments.
Cloud & Modern Test Infrastructure
Experience containerizing test solutions with Docker and orchestrating execution on Kubernetes.
Infrastructure-as-Code and Environment-as-Code using Terraform, Ansible, ARM/Bicep, or CloudFormation.
CI/CD implementation using Jenkins, Azure DevOps, GitHub Actions, or GitLab CI.
Experience building ephemeral test environments, distributed test execution, and cloud-based test labs.
Strong knowledge of Azure, AWS, or Google Cloud, including compute, networking, identity, storage, databases, messaging, and security services.
Experience with observability platforms (Prometheus, Grafana, ELK/OpenSearch, Datadog, Application Insights, OpenTelemetry).
Expertise in performance engineering, resilience testing, chaos engineering, disaster recovery validation, cloud security testing, secrets management, and hybrid/cloud deployment validation.
Preferred Qualifications
Experience with Proficy, Industrial Software, SCADA, MES, Historian, or other OT/Industrial platforms.
ISTQB Advanced Test Automation Engineer or Technical Test Analyst certification.
Azure, AWS, Kubernetes (CKA), VMware, AI/ML, or cloud-related certifications.
Experience with MLOps platforms, model registries, service virtualization, contract testing, security testing, accessibility validation, mobile automation, and SRE practices.

Work arrangement
Hybrid