ANSR is hiring for one of its clients.
About ANSR MedTech:
Who We Are:
ANSR MedTech Capability Center is a new global innovation hub being established in India for a Fortune 100 Fastest-Growing Company in the MedTech sector. Built in partnership with ANSR, the center draws on ANSR's proven experience in establishing and scaling high-performance Global Capability Centers (GCCs) for leading global enterprises.
ANSR MedTech center brings together world-class engineering, product, and technology talent to build next-generation healthcare platforms and solutions that power global operations.
Our Vision:
To build a next-generation MedTech capability center that powers global healthcare innovation.
We envision:
- High-impact innovation hubs shaping global product and technology roadmaps.
- Centers that go beyond support functions to drive core engineering and platform development.
- Sustainable, scalable ecosystems that nurture world-class MedTech talent.
- Capability centers that directly influence patient outcomes worldwide.
- At its core, the ANSR MedTech Capability Center is about enabling innovation that touches lives at scale.
About R&D:
The R&D organization at ANSR MedTech is being built to support the development, testing, and long-term sustainment of complex, regulated products used at global scale.
This team plays a critical role in engineering excellence, product quality, and operational reliability, working closely with global R&D leaders to deliver against well-defined technical standards, quality systems, and product outcomes.
Unlike a traditional offshore or support model, R&D in India is designed to take on meaningful ownership across the product lifecycle, particularly in areas where scale, focus, and execution discipline are essential.
What Makes This Opportunity Stand Out:
- Work on real, production grade products that require high engineering rigor, reliability, and regulatory discipline.
- Be part of a team that spans embedded software, mobile applications, systems engineering, test automation, and lifecycle engineering.
- Contribute to end-to-end R&D workflows—from development and verification through sustaining engineering and triage.
- Help build and scale a new R&D capability from the ground up, shaping labs, automation, ways of working, and quality culture.
- Partner directly with senior global R&D leaders and engineers, gaining exposure to how products are built and operated at enterprise scale.
How the R&D Team Operates:
- Teams are based in India and embedded in day-to-day delivery, not isolated support functions.
- Technical direction, quality standards, and product outcomes are globally aligned, with strong local execution and accountability.
- The organization is built under a Build–Operate–Transfer (BOT) model with ANSR, with responsibilities expanding as the site matures.
- Success is measured by engineering quality, predictable delivery, adherence to quality systems, reduced defects over time, and strong collaboration with global R&D teams.
Role Overview:
Lead verification and validation of cloud-native and AI-powered data platforms. This is an execution-focused leadership role responsible for test strategy, automation, performance, and quality outcomes. Partner with Engineering Managers and Engineering Leads to ensure systems are reliable, scalable, and meet regulatory standards. Requires strong understanding of cloud, data, and AI concepts while focusing on execution and team performance.
Key Responsibilities:
- Own end-to-end test delivery (planning, automation, execution, and reporting)
- Drive automation-first strategy across APIs, microservices, and streaming systems
- Define and track quality metrics (defects, test coverage, escape rate, reliability)
- Lead performance, load, and scalability testing for distributed systems
- Ensure high-quality validation of event-driven pipelines and real-time systems
- Drive predictable test execution aligned to sprints and releases
- Partner with development teams to shift quality left and enforce testing standards
- Lead defect triage, root cause analysis, and continuous improvement
- Ensure compliance with FDA, HIPAA, and internal quality standards
AI-Driven Testing & Modern QA:
- Drive adoption of AI-assisted testing (test case generation, intelligent regression, analysis)
- Ensure validation of AI-powered features including AWS Bedrock-based solutions
- Establish approaches for testing AI outputs (accuracy, consistency, reliability)
- Support testing of agent workflows and MCP-based integrations (e.g., MuleSoft)
- Leverage AI tools (Copilot, etc.) to improve test productivity and coverage
Data & Streaming Quality:
- Validate streaming pipelines (Kafka or equivalent) and event-driven architectures
- Ensure data correctness, completeness, and consistency
- Test real-time ingestion, transformation, and processing workflows
- Partner with data engineering teams on data contracts and validation readiness
Team Leadership & Execution:
- Lead and manage a team of SDET engineers
- Set expectations and hold team accountable for quality outcomes
- Coach on automation frameworks, performance testing, and QA best practices
- Build a culture of quality ownership and continuous improvement
- Collaborate across global teams (US + India)
Technical Expectations:
- Strong understanding of cloud-native architectures (AWS)
- Experience testing APIs, microservices, and distributed systems
- Familiarity with streaming systems (Kafka or equivalent)
- Experience with automation frameworks (REST-assured, Selenium, or similar)
- Performance and scalability testing experience
- Working knowledge of AI platforms (e.g., AWS Bedrock) and agent systems
- Understanding of data quality for AI and analytics systems
Qualifications:
- Bachelor's degree in Computer Science or related field
- 10+ years testing experience
- 5+ years leading test teams
- Experience building automation frameworks
- Strong leadership and communication skills
Success Criteria:
- High automation coverage and reduced manual testing
- Improved defect detection and reduced production issues
- Scalable performance testing capabilities
- Strong validation of AI-powered and data systems
- Predictable and high-quality releases
