Mareana
Website:
mareana.com
Job details:
Quality Engineer – Pre-Validation, CSV & AI Quality
Bangalore
5+ years
Role Overview
We are seeking a Pre-Validation and Computerized System Validation (CSV) Expert with 2 to 5 years of experience in GxP validation, quality assurance, and regulated software environments to contribute to the quality engineering and validation of enterprise-grade SaaS and AI-powered products.
The ideal candidate should have strong hands-on experience in risk-based validation, pre-validation activities, requirements assessment, validation strategy, test planning, traceability, IQ/OQ/PQ, data integrity, audit trail assessment, electronic records/signatures, and validation documentation for cloud-based and integrated systems.
The candidate will work closely with Engineering, Product, Testing, Implementation, Quality, Security, and Customer teams to assess requirements and risks early, identify validation gaps, define appropriate verification and testing approaches, review configuration and implementation evidence, and ensure systems are fit for intended use in regulated manufacturing and life-sciences environments.
AI/GenAI – Must have: Hands-on use of AI for requirements analysis, risk assessment, test/OQ script generation, traceability analysis, validation document review, defect investigation, gap identification, and AI-assisted CSV workflows. Strong understanding of the need to independently verify AI-generated outputs and maintain GxP compliance, data integrity, confidentiality, and auditability.
Key Responsibilities
Pre-Validation & Validation Strategy
- Lead and execute risk-based pre-validation activities for product releases, customer implementations, configuration changes, integrations, and new functionalities in regulated environments.
- Review business, user, functional, and configuration requirements to identify validation impact, GxP relevance, critical functionality, data integrity risks, and test coverage needs.
- Define validation approaches, scope, deliverables, acceptance criteria, and verification strategies based on intended use, system risk, complexity, and regulatory impact.
- Review and challenge Configuration Specifications, functional specifications, design/configuration documentation, and implementation details to confirm that the configured system aligns with approved requirements.
- Perform validation readiness assessments before formal OQ/PQ and identify gaps in requirements, configuration, test data, environments, traceability, documentation, and evidence.
- Support dry runs and pre-validation testing to identify defects, ambiguous requirements, usability gaps, missing scenarios, and OQ script/data-sheet issues before formal execution.
- Review release and customer-specific changes for validation impact and provide risk-based recommendations on regression testing, revalidation, or additional verification.
Computerized System Validation (CSV) & GxP
- Apply CSV lifecycle principles to SaaS, web applications, APIs, databases, integrations, and enterprise platforms used in GxP-regulated processes.
- Develop and review validation deliverables including validation plans/strategies, risk assessments, URS/FRS or equivalent requirements, configuration specifications, traceability matrices, test scripts, test evidence, summary reports, and validation closure documentation.
- Strong understanding of IQ, OQ, PQ and risk-based testing principles, including appropriate separation of verification activities from formal qualification execution.
- Review and assess system functionality related to audit trails, user roles and access control, electronic records, electronic signatures, data retention, data export, data integrity, and system-generated reports.
- Apply principles of 21 CFR Part 11, EU GMP Annex 11, GxP, ALCOA+ data integrity, and applicable computerized-system controls when reviewing validation scope and evidence.
- Review deviations, defects, test failures, change controls, CAPA actions, and validation impacts; assess whether issues affect intended use, data integrity, or validation status.
- Support periodic review, change impact assessment, release certification, and revalidation activities for validated systems.
Pre-Validation Testing, OQ Readiness & Quality Engineering
- Review and challenge test scenarios, OQ scripts, datasheets, expected results, and evidence requirements for completeness, accuracy, traceability, and risk coverage.
- Perform functional, integration, regression, exploratory, configuration, and end-to-end testing as part of pre-validation and validation readiness activities.
- Validate critical business workflows, configuration rules, calculations, data flows, integrations, reports, exports, audit trails, and role-based behavior against approved requirements.
- Identify edge cases, negative scenarios, boundary conditions, dependencies, data inconsistencies, and usability gaps that may not be adequately covered in formal test scripts.
- Ensure test evidence is objective, reproducible, attributable, contemporaneous, complete, and suitable for regulated validation records.
- Partner with Engineering and Testing teams to perform root-cause analysis and confirm defect fixes through appropriate regression and impact-based testing.
AI/GenAI Validation & AI-Assisted CSV
- Use AI/GenAI tools such as ChatGPT, Claude, Copilot, or equivalent tools to accelerate requirements analysis, risk identification, test scenario generation, OQ script drafting, traceability mapping, document review, and defect analysis.
- Design AI-assisted workflows that convert requirements and test cases into structured validation scenarios, OQ steps, expected results, traceability mappings, and validation documentation while retaining human review and approval.
- Evaluate AI-generated test cases and validation content for correctness, completeness, hallucinations, unsupported assumptions, regulatory suitability, and alignment with approved requirements.
- Apply AI to analyze large volumes of requirements, test results, defects, logs, audit-trail data, and validation evidence to identify trends, gaps, inconsistencies, and potential quality risks.
- Understand validation considerations for AI-enabled and AI/ML functionality, including intended use, risk classification, model/output reliability, data quality, explainability, human oversight, change management, and ongoing monitoring where applicable.
- Establish appropriate controls for the use of AI with GxP and customer data, including confidentiality, privacy, data minimization, secure prompting, output verification, and prevention of uncontrolled use of production or sensitive data.
- Contribute to AI-enabled Quality Engineering initiatives and reusable tools that improve validation efficiency, traceability, test coverage, and quality without compromising compliance.
Domain Knowledge – Manufacturing, Life Sciences & Enterprise Systems
- Strong understanding of GxP-regulated manufacturing or life-sciences processes and the role of computerized systems in supporting critical business and quality processes.
- Understand common enterprise systems and data flows such as ERP, MES, LIMS, QMS, WMS, supply chain, batch/material processes, genealogy, master data, reporting, and analytics platforms.
- Ability to understand manufacturing workflows, material and batch relationships, process hierarchies, genealogy/traceability, data lineage, and business-critical configuration rules.
- Assess the impact of system configuration and software changes on business processes, data integrity, downstream integrations, reporting, and validated state.
- Translate business and domain knowledge into practical validation scenarios, risk assessments, traceability, and meaningful test coverage.
Documentation, Compliance & Stakeholder Collaboration
- Review validation and quality documentation for consistency, completeness, traceability, clarity, and alignment with approved procedures and regulatory expectations.
- Maintain strong traceability between requirements, risks, configuration, test cases, OQ/PQ evidence, defects, and final validation conclusions.
- Prepare clear validation observations, risk assessments, review comments, certification recommendations, and release-readiness assessments.
- Work closely with Engineering, Product, Testing, Implementation, CSV, Security, and Customer teams to resolve validation gaps and ensure timely delivery.
- Support customer audits, internal quality reviews, inspection readiness, and responses to validation-related questions when required.
Qualifications & Experience
- Bachelor's or Master's degree in Computer Science, Software Engineering, Life Sciences, Pharmacy, Engineering, or a related field.
- 2 to 5 years of experience in CSV, Computer System Validation, Quality Assurance, Quality Engineering, Validation, or a closely related regulated environment.
- Strong hands-on experience with GxP validation and risk-based testing for SaaS, web applications, APIs, databases, integrations, or enterprise systems.
- Strong understanding of 21 CFR Part 11, EU GMP Annex 11, GxP principles, ALCOA+ data integrity, audit trails, electronic records, and electronic signatures.
- Hands-on experience with validation documentation, requirements, risk assessments, traceability matrices, IQ/OQ/PQ, test evidence, deviations, change controls, and validation reports.
- Experience with Agile/Scrum software development and working with Engineering, Product, Testing, DevOps, Implementation, and Quality teams.
- Experience with JIRA, Confluence, or similar tools for requirements, defects, documentation, and traceability.
- Hands-on experience using AI/GenAI tools for quality and validation activities, with strong ability to critically review and validate AI-generated outputs.
- Experience in manufacturing, life sciences, pharmaceuticals, chemicals, food & beverage, consumer goods, or other regulated industries is strongly preferred.
Skills & Attributes
- Strong understanding of CSV lifecycle, risk-based validation, GxP controls, and data integrity principles.
- Strong analytical and problem-solving skills with the ability to assess validation risk and determine appropriate testing depth.
- Ability to understand complex business workflows, system configurations, integrations, data models, and downstream impacts.
- Strong requirement-to-test traceability and validation documentation skills.
- Ability to identify functional gaps, edge cases, configuration risks, data integrity risks, and compliance gaps.
- Strong understanding of audit trails, role-based access, electronic records/signatures, data retention, and controlled changes.
- Ability to independently review OQ scripts and datasheets and identify missing coverage, ambiguous steps, incorrect expected results, and evidence gaps.
- Strong AI literacy with the ability to use AI as a productivity and quality-engineering accelerator while maintaining human oversight and regulatory accountability.
- Ability to assess AI-generated content for accuracy, hallucination, completeness, bias, confidentiality, and suitability for regulated use.
- Good understanding of manufacturing processes, enterprise data models, and GxP business workflows.
- Strong attention to detail and a quality-first mindset.
- Good communication, stakeholder management, and technical documentation skills.
- Ability to work effectively in a fast-paced product engineering environment and manage multiple validation priorities.
What you will get to work on:
- Opportunity to work with AI-powered enterprise platforms and regulated digital transformation initiatives.
- Opportunity to work at the intersection of CSV, Quality Engineering, AI, Manufacturing, and Life Sciences.
- Culture focused on innovation, learning, quality, compliance, and technical excellence.
- Regular workshops, training programs, and tech talks.
About Mareana
Mareana builds cutting-edge enterprise digital AI products focused on Manufacturing, Supply Chain, and Sustainability. Our AI-driven platforms solve complex data challenges and deliver real-time insights, enabling businesses to accelerate digital transformation.
We have delivered more than $1B+ in business value to global clients, including leading firms in life sciences, consumer goods, chemicals, food & beverage, and manufacturing. We are recognized by Gartner as a “Cool Vendor in AI” and have been featured in Silicon Review and Manufacturing Insights.
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