New opportunity

AI/Data Science Engineer II

Medtronic · Hyderabad, India

About this role

The Data Science and Artificial Intelligence function at Medtronic develops intelligent software solutions that combine machine learning, artificial intelligence, data analytics, and software engineering to address business and product challenges. As an AI Data Science Engineer II, you will design, develop, deploy, and support AI-enabled applications that combine prompt engineering, traditional machine learning, and full stack software development. You will collaborate with technical and non-technical stakeholders to deliver scalable, maintainable, and audit-defensible solutions while supporting application operations and continuous improvement initiatives. Responsibilities: Design, evaluate, and optimize prompts for LLM and AI-enabled applications to improve accuracy and user experience. Develop and maintain full stack applications using Python, Vue, JavaScript, and TypeScript. Integrate AI and machine learning solutions into production-grade software. Apply machine learning techniques including data preparation, feature engineering, model development, validation, and performance evaluation. Manage application deployment and troubleshooting within IIS and Windows-based server environments. Produce technical documentation to support traceability, reproducibility, and audit readiness. Required Qualifications: Bachelor's degree with 4+ years of experience or Master's degree in Computer Science, Data Science, AI, Software Engineering, IT, or Machine Learning. Proficiency in Python, full stack development, prompt engineering, and traditional machine learning techniques. Experience with Vue, JavaScript/TypeScript, IIS, and server management. Preferred Qualifications: Experience in regulated or quality-driven environments, CI/CD, DevOps practices, cloud or hybrid computing, and providing technical leadership or mentoring.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonVueJavaScriptTypeScriptPrompt EngineeringLarge Language Models (LLMs)Machine LearningData PreparationFeature EngineeringModel DevelopmentModel ValidationIISWindows ServerFull Stack DevelopmentTechnical Documentation

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Full-Stack AI Integration
The role requires bridging the gap between AI model development and production-grade software. You must be proficient in Python for backend logic and machine learning, while simultaneously managing frontend interfaces using Vue, JavaScript, and TypeScript to ensure seamless user experiences.
Audit-Defensible Documentation
Operating in a healthcare technology environment necessitates rigorous documentation. You must produce technical records that support traceability, reproducibility, and audit readiness for all software, models, and operational workflows to meet regulatory standards.
Prompt Engineering and Optimization
A core responsibility is the design and evaluation of prompts for LLM-enabled applications. You are expected to iterate on these prompts to improve accuracy, consistency, and overall performance within the application ecosystem.
Infrastructure and Deployment Management
You are responsible for the end-to-end lifecycle of applications, including deployment and troubleshooting within IIS and Windows-based server environments. This requires a strong grasp of server management and operational support.

Likely challenges to think through

Balancing Innovation with Regulatory Rigor
The challenge lies in implementing cutting-edge AI solutions while adhering to strict quality-driven and regulated environments. Prepare by demonstrating how you maintain agility in development without compromising on the documentation and traceability required for medical technology compliance.
Cross-Functional Stakeholder Communication
You will need to translate complex technical concepts, risks, and dependencies for both technical and non-technical stakeholders. Prepare by practicing how to explain the business value and operational impact of AI initiatives to non-technical partners.
Managing Legacy and Modern Environments
The requirement to work with IIS and Windows-based servers alongside modern AI and full-stack frameworks may present integration hurdles. Prepare by highlighting your experience in maintaining stability in traditional server environments while deploying modern, scalable AI applications.

Practice your answers

Technical practice

Prompt Engineering Strategies for LLMs

What to coverDescribe your approach to optimizing prompts for accuracy and consistency in production environments. Discuss techniques like few-shot prompting, chain-of-thought, or system-level instructions and how you evaluate the performance of these prompts.

Full Stack Integration of AI Models

What to coverExplain how you integrate machine learning models into a full stack application using Python and Vue/JavaScript. Focus on the architecture for handling asynchronous requests, managing model inference latency, and ensuring the frontend remains responsive.

Machine Learning Lifecycle and Validation

What to coverDetail your process for moving a model from development to production, specifically regarding data preparation, feature engineering, and model validation. Emphasize how you ensure the model remains performant and reliable after deployment.

Deployment and Troubleshooting in IIS

What to coverDiscuss your experience managing application deployments within IIS and Windows Server environments. Explain how you approach troubleshooting common deployment issues, such as configuration errors, permission problems, or performance bottlenecks.

Ensuring Audit-Defensible AI Solutions

What to coverIn a regulated environment, how do you ensure your AI solutions are audit-defensible? Explain the importance of documentation, traceability, and reproducibility in your development workflow.

Managing Scalability in AI Applications

What to coverHow do you design AI-enabled applications to be scalable? Discuss strategies for handling increased load, optimizing resource usage, and ensuring the system remains maintainable as it grows.

CI/CD and DevOps for AI

What to coverDescribe your experience with CI/CD pipelines in the context of AI and machine learning. How do you automate testing and deployment to ensure consistent software releases while maintaining model quality?

Frontend Development with Vue and TypeScript

What to coverExplain how you leverage Vue and TypeScript to build robust user interfaces for data-driven applications. Discuss how you manage state and data flow when interacting with backend AI services.

Behavioural practice

Collaborating with Non-Technical Stakeholders

What to coverDescribe a time you had to explain a complex technical concept or AI model performance to a non-technical stakeholder. How did you ensure they understood the risks, dependencies, and project status?

Technical Leadership and Mentoring

What to coverProvide an example of when you provided technical leadership or mentored a team member. How did you guide them through a technical challenge or help them improve their development practices?

Navigating Regulated Environments

What to coverTell me about a time you worked in a quality-driven or regulated environment. How did you balance the need for rapid development with the strict requirements for documentation and traceability?

Handling Project Risks and Dependencies

What to coverDescribe a situation where you identified a significant risk or dependency in an AI project. What actions did you take to mitigate the risk and communicate the impact to the broader team?

Sharpen your knowledge

Topics to prepare

Audit-Defensible AI Development
The role emphasizes traceability, reproducibility, and audit readiness in a regulated environment. Exercise: Create a documentation template for an ML project that includes data lineage, model versioning, and validation logs to ensure compliance with quality standards.
Prompt Engineering for Enterprise Applications
The position requires designing and optimizing prompts for LLMs to improve accuracy and user experience. Exercise: Develop a structured prompt evaluation framework that measures response consistency and accuracy against a set of predefined test cases.
Production-Grade ML Integration
Focus on integrating ML models into full-stack software. Exercise: Review the lifecycle of a model from feature engineering to deployment, specifically focusing on how to handle model performance monitoring and retraining within a production environment.

Skills to revise

Full Stack Development with Vue and Python
The role requires building and maintaining applications using Python, Vue, JavaScript, and TypeScript. Exercise: Build a simple dashboard using Vue.js that consumes a Python-based API to display real-time data or model inference results.
IIS and Windows Server Deployment
The role involves managing deployments within IIS and Windows-based environments. Exercise: Practice deploying a Python-based web application to an IIS server, including configuring the necessary handlers and environment variables for production readiness.
CI/CD and DevOps Practices
Preferred qualification involves experience with CI/CD and software release processes. Exercise: Design a basic CI/CD pipeline configuration that automates testing and deployment for a Python application, ensuring that code changes are validated before reaching production.

Your preparation checklist

Prepare a portfolio of full-stack AI applications
Develop a project that demonstrates integration of a machine learning model into a web application using Python for the backend and Vue/JavaScript/TypeScript for the frontend. Ensure the project includes clear documentation on how the AI component was integrated and tested.
Document your experience with prompt engineering
Create a case study or summary of how you have optimized prompts for LLMs to improve accuracy or user experience. Be prepared to discuss your methodology for evaluating prompt performance and how you handle consistency in AI-enabled outputs.
Review documentation standards for regulated environments
Prepare examples or explanations of how you have maintained traceability, reproducibility, and audit readiness in past technical projects. Familiarize yourself with the importance of documentation in healthcare or quality-driven industries.
Refine your knowledge of IIS and Windows server deployment
Review your experience with deploying and troubleshooting applications within IIS and Windows-based server environments. Be ready to explain your process for managing application operations and ensuring system stability.
Formulate questions regarding team collaboration and technical leadership
Prepare thoughtful questions about how the team balances technical innovation with regulatory requirements, how they handle cross-functional communication with non-technical stakeholders, and what opportunities exist for technical mentoring or leadership.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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