Full-Time

LLM Application Engineer

Artificial Intelligence

Ginas Tech Jobs

Ginas Tech Jobs

Compensation Overview

$130k - $175k/yr

San Francisco, CA, USA

Remote

Category
Software Engineering (1)
Required Skills
LLM
Python
Distributed Systems
PyTorch
Observability
REST APIs
Requirements
  • Artificial Intelligence experience is required.
  • Strong software engineering fundamentals and experience building AI-powered applications are required.
  • Hands-on experience with large language models, generative AI, or agent-based systems is required.
  • Experience designing prompts, workflows, evaluations, or AI behavior is required.
  • Ability to write clean, production-quality code is required.
  • Ability to work across abstraction layers from model to system to product is required.
  • Experience with Python, large language model APIs and model providers including OpenAI-compatible APIs and open-weight models, agent frameworks and orchestration systems, vector databases and retrieval systems, backend services, APIs, distributed systems, PyTorch, or JAX is required.
Responsibilities
  • Build and ship large-language-model-powered applications and artificial intelligence agent workflows.
  • Design systems for reasoning, planning, memory, tool use, and multi-step execution.
  • Build reliable orchestration pipelines that turn probabilistic model outputs into predictable, observable, and safe actions.
  • Integrate large language models with APIs, databases, search, internal services, and external tools.
  • Develop prompting, context engineering, structured outputs, tool-calling, and other techniques to improve model behavior.
  • Build evaluation frameworks and datasets to measure artificial intelligence quality, reliability, and regressions.
  • Debug artificial intelligence systems across the stack, from model behavior and prompts to orchestration, backend services, and product user experience.
  • Optimize artificial intelligence systems for quality, latency, and cost.
  • Work with product and engineering teams to turn ambiguous product problems into working artificial intelligence solutions.
  • Establish production practices for observability, tracing, experimentation, evaluation, and continuous improvement.

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