Tesla

The Role

We are seeking a motivated Software Engineer to join the IT Application Business Intelligence team at Tesla Shanghai Gigafactory. You will help build the next generation of data products: LLM Agents that work with complex data structures, large data volumes, and interactive analytics with business users—conversational, tool-augmented analysis on enterprise data platforms, not static dashboards alone.

LLM Agents are changing how enterprises use data. Enterprise data warehouses and analytics platforms must evolve—new architectures, access patterns, and performance engineering for agent-driven interaction (tool calls, multi-turn dialogue, high concurrency), not only traditional reporting. You will work on both Agent engineering and this platform transformation.

You have a strong track record of building and deploying LLM Agents, data analysis engineering, and backend services on data warehouse and big-data platforms. You are comfortable with agent-oriented storage, knowledge bases, and dialogue/reasoning (RAG, tool use, session context, guardrails). This is a production software engineering role.

Responsibilities

• Build, deploy, and operate LLM Agents for factory and enterprise analytics: tool calling, workflows, evaluation, and production monitoring.
• Develop backend services and APIs connecting Agents to data warehouses, metrics layers, and big-data platforms (SQL and governed datasets, data APIs, standard enterprise access patterns).
• Deliver interactive data analysis with users: multi-turn dialogue, intent clarification, large-scale and structured data, and explainable outcomes.
• Own agent infrastructure: knowledge bases, retrieval/RAG, search or vector indexes as needed, conversation state, permissions, and audit trails.
• Evolve enterprise data warehouse and serving architecture for Agent interaction: redesign access layers, APIs, and governance; optimize performance for tool-based, conversational analytics (latency, throughput, cost, and reliability)—in partnership with data engineers, with clear boundaries and reusable capabilities rather than one-off prompts.
• Integrate Agents with internal products across manufacturing, supply chain, and government/compliance data services, and uphold production standards (CI/CD, observability, on-call as agreed).
• Implement services in Python (FastAPI/Flask) and/or Java (Spring Boot); collaborate with frontend engineers on React/portal UX when needed (not a frontend-only hire).
• Communicate effectively in English and Chinese with factory, IT, and HQ stakeholders.

Requirements

Must Qualifications

• 3 to 5 years of software engineering with production systems shipped and maintained.
• Proven experience building and deploying LLM Agents (tools, orchestration, deployment, iteration—not prototypes only).
• Strong data analysis engineering: complex SQL/analytics, large datasets, metrics and data quality—not ML model training as a primary skill.
• Hands-on agent data layer: knowledge bases, retrieval/RAG, conversation and reasoning flows, session/memory, logging and safety for LLM outputs.
• Data warehouse or big-data platform development or operations (e.g. Hive, Spark, Airflow, distributed SQL engines, ClickHouse-class OLAP, or comparable enterprise stacks), including exposure to platform changes driven by analytical or Agent workloads.
• Solid backend skills in Python and/or Java (REST APIs, databases, distributed systems fundamentals).
• Strong problem-solving, ownership, and teamwork.

Preferred Qualifications

• Project experience in manufacturing, supply chain, or government/regulatory data services.
• Building and operating big-data platforms (ingestion, scheduling, serving, reliability).
• Industry background in new energy vehicles, robotics, or industrial machinery manufacturing.
• React/Next.js or internal portals (collaboration; not the primary hiring bar).
• Docker/Kubernetes, CI/CD, Elasticsearch/OpenSearch, or vector databases when used in Agent or analytics platforms.
• Experience with large-scale enterprise data platforms in global manufacturing, automotive, or technology organizations.
• Track record of architectural or performance improvements on warehouses or analytics platforms to support LLM Agents, conversational analytics, or high-interaction query patterns.