Senior Backend Engineer

MSI Data-posted 7 days ago

Full-time • Senior

San Francisco, CA

51-100 employees

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MSI Data is launching a new, dedicated AI team with a singular mission: to disrupt the Field Service software category through AI-native solutions. We are moving beyond simple automation to create intelligent, conversational, and agentic experiences that empower technicians in the field. As a Senior Backend Engineer and early member of this founding team, you will be the engine behind our technical delivery. Working alongside our Principal AI Architect, you will focus on high-productivity execution—translating ambitious technical vision into robust, production-ready code. You are a builder first. You have shipped AI solutions before, and you know how to bridge the gap between complex AI models and the hands of users. This is a role for a developer who loves to write code, solve difficult implementation challenges, and drive the team’s daily momentum.

Architect Data Pipelines: Design and build scalable ingestion pipelines that handle massive datasets with low latency.
Build Connectors: Develop and maintain robust connectors for third-party systems, ensuring seamless data synchronization.
Background Processing: Implement and optimize background job processing systems to handle asynchronous tasks and heavy computational loads without impacting user experience.
System Integration: Lead the technical strategy for integrating disparate data sources into a unified AI-ready architecture.

Agentic Backend Development : Expert proficiency in Python or TypeScript with production experience using agentic frameworks (e.g., LangChain, LlamaIndex, AutoGen) to orchestrate multi-step workflows.
RAG & Vector Ops : Demonstrated ability to build ETL pipelines for unstructured data (ingestion, chunking, embedding) and manage vector databases at scale (e.g., Pinecone, Weaviate, pgvector).
High-Scale Data Integration : Strong experience architecting resilient connectors for third-party SaaS APIs (Salesforce, Jira, etc.), handling OAuth, rate limiting, and data consistency. Experience with relational databases like PostgreSQL.
LLM Tooling : Experience abstracting backend APIs into "Tools" or Function Calls that allow Large Language Models to autonomously execute actions and retrieve live data.
Async Architecture : Mastery of background job processing and event-driven patterns (using Kafka, RabbitMQ, or Celery) to manage long-running AI inference tasks without blocking user interactions.

Base pay is just one part of your total compensation. This role includes a target annual bonus and meaningful equity participation . Final compensation packages are tailored to the candidate’s experience, qualifications, and location.

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