Software Engineer - AI/LLM Applied

職務概要

The Opportunity We are building data analytics agents that leverage LLMs to help users extract insights from our data platform (Databricks). These agents must go beyond natural-language interfaces—they are expected to reason over data, generate correct queries, interpret results, and deliver actionable insights that business users can trust. This role is responsible for designing systems where LLMs interact safely and reliably with production data, under real-world constraints such as data scale, schema complexity, governance, and performance. Key Responsibilities - Architect and Scale Core Systems: Lead the design, implementation, and optimization of highly scalable, fault-tolerant backend services and non-blocking APIs using Python (FastAPI) . Ensure services handle high throughput with minimal latency. - End-to-End Product Delivery: Drive the full lifecycle of product features—from requirements gathering and prototyping through testing, deployment, and iteration—working closely with product managers, UX designers, and DevOps. - Champion Asynchronous Patterns: Build and maintain event-driven, asynchronous processing pipelines (e.g., message queues, pub/sub) to support real-time data ingestion, processing, and integrations. - Performance & Reliability: Establish and enforce best practices for monitoring, observability, and automated testing. Identify bottlenecks and implement caching, connection pooling, backpressure, and rate-limiting strategies to sustain 99.9%+ uptime. - Comprehensive Service Monitoring: Design, deploy, and maintain end-to-end observability solutions using Prometheus for metrics collection and Grafana for visualization and alerting. - AI & RAG Applications: Architect Retrieval-Augmented Generation (RAG) solutions and intelligent agents that solve real-world problems—integrating vector stores, LLM APIs (e.g., OpenAI), and custom prompting frameworks. - Mentorship & Collaboration: Provide technical leadership and mentorship to mid-level engineers, foster a culture of code quality, and collaborate across cross-functional teams to drive continuous improvement.