Senior AI Full Stack Engineer

We are hiring for a health-tech company. We are seeking an experienced Senior AI Full Stack Engineer to take end-to-end production ownership of complex web applications. You will build and scale high-throughput systems, architect reliable databases for real-time workflows, and leverage modern AI engineering workflows to accelerate product delivery without sacrificing code quality or system reliability.

Key Responsibilities

• Production Ownership: Design, architect, ship, and maintain scalable web applications and distributed backend microservices.
• Full-Stack Development: Build modern, accessible user interfaces paired with robust, performant APIs and background workers.
• Data Architecture: Design complex SQL schemas, write efficient queries, and optimize databases for real-time data ingestion and transactional consistency.
• Infrastructure & Ops: Deploy, monitor, and maintain cloud services on AWS or GCP using CI/CD pipelines and infrastructure best practices.
• AI-Augmented Engineering: Systematically integrate AI tools into your daily development workflow—maximizing velocity while applying rigorous verification to ensure system safety and accuracy.

Qualifications

• 5+ years of professional software engineering experience owning production web applications end-to-end.
• Frontend: Strong expertise in TypeScript and modern UI frameworks (React preferred).
• Backend: Advanced proficiency in at least one backend ecosystem: Java/Kotlin, Python, or Go.
• Databases: Strong SQL proficiency and deep experience with schema design for complex, real-time workflows.
• Cloud & DevOps: Demonstrated experience deploying and operating applications on AWS or GCP.
• AI Tool Fluency: Daily, critical usage of modern AI coding assistants (e.g., Copilot, Cursor, Claude Code). You know precisely where LLMs excel, where they hallucinate or fall short, and how to verify, test, and refine generated code.

Nice-to-Haves

• Experience building or integrating custom LLM chains, agentic frameworks, or RAG pipelines.
• Knowledge of web sockets, event-driven architectures, or queueing systems (e.g., Kafka, Redis, SQS).
• Experience with Infrastructure as Code (e.g., Terraform, AWS CDK).