Popai Health- AI Engineer

Description

Popai is on a mission to transform the millions of conversations that happen between care teams and patients every day. We're building the AI platform that turns every patient interaction into action - helping care teams understand what patients need, close gaps in care, and reach the people who need them most. Our goal is simple but ambitious: make he

As an AI Engineer at Popai, you will design, build, and deploy intelligent systems that power our core product. You will work across the entire lifecycle - from building robust data pipelines for medical records and audio data to operationalizing cutting-edge LLMs and agentic workflows in production.

What You'll Do

• Build AI & Data Pipelines: Design, build, and maintain data and AI pipelines that ingest, process, and analyze large-scale healthcare data (including medical records, audio signals, and system traffic).
• Develop Intelligent Systems: Build systems that extract structured entities, definitions, and actionable insights from complex unstructured data.
• Deploy AI Agents: Operationalize and integrate AI agents using LLMs, open-weight models, and specialized tools into production systems to automate reasoning and care workflows.
• Optimize Performance: Optimize model performance, inference pipelines, and latency to ensure high scalability and efficiency.
• Ensure Data Quality & Observability: Design reliable data workflows with built-in quality checks, consistency, and monitoring across all pipeline stages.
• R&D & Innovation: Experiment with emerging AI frameworks, model architectures, and agentic workflows to continuously elevate the platform's intelligence.

Requirements

What We're Looking For

• Experience: 6+ years of hands-on experience as a Software Engineer, AI Engineer, or Data Engineer.
• Scalability & Systems: Proven track record of working on high-scale, distributed systems with a focus on performance optimization and resilience.
• Data Infrastructure: Solid understanding of data processing and storage technologies (e.g., PostgreSQL, BigQuery, Snowflake, Spark, Kafka).
• AI & LLM Deployment: Practical experience integrating, serving, or fine-tuning LLMs and smaller specialized models (API-based or open-weight).
• Cloud & DevOps: Proficiency with cloud platforms (AWS, GCP, or Azure) and containerized setups (Docker, Kubernetes).

Nice to Have

• Experience with LLM serving optimizations, quantization, prompt engineering, or agent orchestration frameworks (e.g., LangChain, LangGraph).
• Familiarity with audio/speech processing or medical record data systems.
• Experience designing event-driven, streaming architectures.
• Background in real-time inference or low-latency AI applications.