AI Software Engineer
Summary
Builds full-stack cloud-native apps that integrate GenAI models into healthcare systems, using Java/Spring Boot, Python, React, and AWS/Azure.
Overview
The Full Stack Application Engineer will develop and maintain data-intensive applications that powers GenAI solutions, transforming healthcare data into actionable insights at a national level.
In this role, the candidate will collaborate with AI engineers, data scientists, and business stakeholders to build scalable, cloud-native applications, with a strong focus on GenAI integration and Data Analytics.
Responsibilities
- Develop, test, deploy, and maintain full stack applications supporting analytics and GenAI use cases.
- Build APIs and interfaces to integrate AI/GenAI models into healthcare systems.
- Collaborate with data engineering teams to ensure robust data pipelines, optimized for AI workloads.
- Implement best practices in cloud-native development, CI/CD, security, and DevOps to ensure high-quality, scalable solutions.
- Monitor application health and basic observability metrics (logs, metrics, traces); escalate anomalies and support root-cause analysis.
- Maintain clear technical documentation (runbooks, design notes, API docs) and contribute to knowledge-sharing sessions.
- Work closely with AI/GenAI teams to operationalize models and deliver AI-powered features.
- Contribute to service monitoring, incident management, and reporting.
Qualifications
- 5+ years of experience in full stack application development with strong hands-on exposure to both frontend and backend.
- Cloud Service Provider (CSP) experience: Proficiency in AWS and Azure platforms, including native cloud services and databases (e.g., DynamoDB, Cosmos DB, RDS, etc.).
- Strong backend experience in Java (Spring Boot) and Python for building APIs and microservices.
- Proficiency in frontend development with frameworks such as React (preferred), Angular, or Vue.js, along with Type , Java , HTML5, and CSS.
- Experience with relational databases and SQL; exposure to NoSQL (e.g., MongoDB) or data warehouses is useful.
Desirable Skills
- Experience operationalizing AI/ML/GenAI models in production.
- Familiarity with AI/ML services on AWS (e.g., Bedrock, SageMaker) or Azure (e.g., Azure ML, OpenAI Service).
- Prior experience with national-scale, public sector, or healthcare projects is an advantage.