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.