Applied AI Engineer (AI)

TL;DR

Applied AI Engineer (AI): Building and deploying full-stack AI-powered applications and integration accelerators with an accent on agentic workflows, RAG patterns, and production-quality interfaces. Focus on translating PoC blueprints into operational software, implementing observability layers, and ensuring system reliability under production conditions.

Location: Remote (USA, Canada, Europe, or Australia)

Salary: $130,000–$160,000 USD / $110,000–$130,000 CAD

Company

A global data and analytics company powering auto-related decisions through proprietary data, advanced analytics, and seamless workflows.

What you will do

• Lead application architecture and implementation for AI-powered full-stack applications on 2-to-4-week sprint cycles.
• Translate PoC blueprints into production-ready applications using agentic frameworks and vector database integrations.
• Build evaluation harnesses and observability instrumentation to ensure AI behavior is inspectable and trustworthy.
• Integrate outputs into core data infrastructure including Snowflake and internal API gateways.
• Collaborate with product and engineering teams to scope new requests and define measurable success criteria.
• Own the full lifecycle of AI outputs from development through deployment and handoff to maintaining teams.

Requirements

• Must be based in the USA, Canada, Europe, or Australia.
• Proven portfolio of shipped, production-grade AI applications.
• Full-stack engineering depth with proficiency in React and backend development (REST/GraphQL).
• Hands-on experience with agentic frameworks or LLM APIs (LangChain, LlamaIndex, Anthropic, OpenAI).
• Experience with RAG patterns, streaming responses, and building intuitive AI interfaces.
• Strong product intuition and ability to manage inference costs as a technical constraint.

Nice to have

• Experience with evaluation frameworks and LLM observability tools like Langfuse, LangSmith, or Braintrust.
• Familiarity with multi-model routing and cost optimization tools like LiteLLM or Portkey.
• Data platform experience with Snowflake or BigQuery.
• Knowledge of security basics for AI applications including PII handling and prompt injection defense.

Culture & Benefits

• Opportunity to work on high-impact AI initiatives at startup velocity within a global corporation.
• Focus on innovation, collaboration, and data-driven decision-making.
• Commitment to a diverse and inclusive workforce.
• Support for professional growth and institutional knowledge sharing.