Understanding the Complexities of Medical Device Engineering: An In-Depth Interview with Sai Teja Nuka
Sai Teja Nuka’s career stretches from designing F1-style race cars to leading validation projects for medical devices. With over five years of hands-on experience, he’s dived deep into quality systems, product design, and validation protocols. While solving a tricky problem on a manufacturing line or writing precise IQ/OQ/PQ reports, he’s always thinking a step ahead.
In this interview, Sai Teja shares his path from student engineer to sustaining mechanical expert. He touches on his favorite projects, biggest challenges, and the moments that shaped his outlook. He reflects on the art of problem-solving in high-stakes environments, be it automotive or medical tech.
Q1: Sai Teja, your 5+ years of experience spanning validation, quality, and design in the medical device sector is impressive. Could you start by sharing what initially drew you to this highly regulated and impactful industry?
Sai Teja: Thank you for the opportunity. What drew me to the medical device industry was the meaningful impact it has on patient outcomes, combined with the challenge of working in a highly regulated space. My engineering background gave me a strong foundation in problem-solving, and the medical field offered a way to apply those skills in the service of human health. The opportunity to blend innovation with compliance—to ensure safety while pushing boundaries—has always been both intellectually and ethically fulfilling for me. Over the years, working in validation, quality, and design roles has deepened my passion for advancing technologies that are not only effective but also safe and accessible.
Q2: Your role at Baxter involved managing cross-functional teams through new product development and EOL change control processes for Class II medical devices. How do you ensure effective collaboration between R&D, manufacturing, and regulatory teams while maintaining compliance with ISO 13485 and FDA 21 CFR Part 820?
Sai Teja: At Baxter, cross-functional collaboration was essential, especially during new product development (NPD) and end-of-life (EOL) change controls for Class II devices. I established structured communication frameworks that included design reviews, risk assessments (per ISO 14971), and stage-gate approvals. By implementing document control systems aligned with ISO 13485 and aligning processes with FDA 21 CFR Part 820, we ensured all teams had clarity on deliverables and regulatory checkpoints. I also served as a liaison between regulatory and engineering to bridge technical documentation with compliance needs, maintaining audit readiness, and smooth design transfer into production.
Q3: Based on “Leveraging AI and Generative AI for Medical Device Innovation,” how can generative AI enhance patient-specific medical device design, and what are the key regulatory hurdles in bringing these custom solutions to market?
Sai Teja: In my publication “Leveraging AI and Generative AI for Medical Device Innovation,” I explore how generative AI can accelerate the creation of patient-specific devices by using patient anatomy data (like CT or MRI scans) to generate optimized device geometries. This approach can reduce design cycles, improve fit and performance, and even anticipate long-term wear or biological response through simulation.
However, the key regulatory hurdles include:
- Traceability of AI-generated designs,
- Validation of algorithms under ISO 13485 and FDA guidelines,
- Clinical justification of safety and efficacy for each custom variant, and
- Cybersecurity and data privacy when using patient-specific data.
Navigating these requires tight integration of regulatory affairs into the design process from the start.
Q4: In your time at Creganna Medical, you worked on contract projects related to manufacturing engineering for NPD. Given the high-pressure nature of contract roles, how did you approach process qualification and troubleshooting when time and resources were limited?
Sai Teja: At Creganna Medical, contract roles demanded speed without compromising compliance. My approach involved prioritizing risk-based qualification, focusing efforts where patient safety or product performance was most at stake. I relied on lean validation principles: using historical data, streamlined protocols, and real-time issue tracking. For troubleshooting, I implemented cross-functional huddles to isolate root causes quickly and leveraged statistical tools for process optimization. Clear documentation and proactive stakeholder communication ensured that we stayed aligned on timelines while meeting regulatory expectations.
Q5: In your LinkedIn profile, you highlight your work as project engineer, particularly in performing gap analyses and ensuring documentation meets FDA and EU MDR standards. Could you walk us through a challenging DHF compliance issue you faced and how you resolved it?
Sai Teja: In one project as a project engineer, we faced a DHF (Design History File) non-conformance during an internal audit. The root issue was incomplete traceability between design inputs and verification results due to a documentation handoff gap. I initiated a comprehensive gap analysis aligned with FDA and EU MDR standards. We created a trace matrix linking user needs, design inputs, risk mitigations, and test protocols. I worked closely with QA and R&D to revalidate critical elements and ensured all corrective actions were documented under CAPA procedures. The revised DHF passed both internal and external audits successfully.
Q6: Referring to “The Role of AI-Driven Clinical Research in Medical Device Development,” how does a data-driven approach using AI improve regulatory compliance and quality assurance in the clinical testing phase of medical devices?
Sai Teja: In “The Role of AI-Driven Clinical Research in Medical Device Development,” I detail how AI improves compliance and QA by enabling:
- Predictive analytics for adverse event detection and risk stratification,
- Automated data cleaning and integrity checks during trials,
- Adaptive trial designs that reduce cost and duration while maintaining statistical power,
- Real-time monitoring dashboards for regulatory documentation and audit readiness.
AI helps not just in faster clinical outcomes, but in creating robust, traceable evidence that aligns with both FDA and MDR expectations, ensuring compliance while accelerating market access.
Conclusion
Talking to Sai Teja feels like taking a tour through a high-performance machine shop; every detail has a purpose. From his early interest in car design to his current work in medical device manufacturing, his approach has always been practical, methodical, and curious. He fixes problems by diving into them, learning from them, and finding better ways forward.
This interview shows a mind constantly tuned to how things work and how they can work better. Sai Teja stays rooted in real-world application while writing validation protocols or sketching out a solution on paper. His story is a reminder that great engineering is all about staying hands-on, asking the right questions, and never losing your love for the craft.
