AI Backend Engineer, Artificial Intelligence (AI) Required, Work From Home

Parallel Partners•San Francisco, CA

20h•$140,000 - $200,000•Remote

About The Position

We are looking for talented AI Backend Engineers to build systems that power AI-driven decisioning, automation, and orchestration across our platform. This role sits at the core of our AI stack, between models, backend systems, and real users, where latency, correctness, reliability, and cost directly impact production experience. This is a fully remote position where you will be part of a global engineering team working across multiple countries to build reliable, scalable AI systems. This position is 100% Remote. MUST BE WILLING TO TAKE A 60 MINUTE CODING ASSESSMENT.

Requirements

Strong backend engineering experience in production systems.
Artificial Intelligence (AI) experience required.
Experience building or operating high-throughput, low-latency services.
Familiarity with AI Systems (LLMs, Embeddings, or AI Workflows).
Experience with distributed systems and production debugging.
Strong understanding of APIs, data flows, and system design principles.
Experience with observability tools (logging, monitoring, tracing).
Strong ownership mindset and bias toward shipping.
Comfortable working in fast-paced, globally distributed teams.
Tech Stack: Anthropic, Claude, Artificial Intelligence (AI), Distributed Systems Tooling, Docker, Kubernetes, LLM APIs, Node.js, NoSQL, Observability Tools, OpenAI, Open-Source Models, Python, SQL.

Responsibilities

Build and operate backend systems that serve AI-powered insurance workflows in production.
Design and implement AI orchestration layers that connect models, APIs, workflows, and business logic.
Build inference pipelines for LLM-based and AI-assisted automation systems.
Optimize latency, throughput, and cost across AI services (caching, batching, streaming, routing).
Design stable service boundaries between backend systems, ML components, and product APIs.
Implement observability: logging, metrics, tracing, alerting, and incident response workflows.
Debug production issues across distributed AI systems and resolve root causes.
Collaborate closely with frontend, product, operations, and ML teams to ship end-to-end features.
Continuously improve system reliability, scalability, and performance.