LLM Application Engineer, Artificial Intelligence (AI) Required, Work From Home (Fully Remote)

Gina's Tech Jobs; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Gina's Tech Jobs seeks an engineer to build and deploy LLM-powered applications and AI agent workflows. You will design systems for reasoning, planning, memory, tool use and multi-step execution, and create orchestration pipelines that translate model outputs into safe, observable actions.

The role emphasizes delivering production-grade code, strong backend services, and scalable, observable AI workflows to drive measurable user impact.

Qualifications

• AI experience required with strong software engineering fundamentals.
• Hands-on experience with LLMs, generative AI, or agent-based systems.
• Ability to write clean, production-quality code and work across abstraction layers from model to product.
• Proficient in Python, LLM APIs and model providers, including OpenAI-compatible APIs and open-weight models.
• Familiar with agent frameworks, orchestration systems, vector databases, and retrieval systems.
• Experience with backend services, APIs, distributed systems, and PyTorch/JAX.

Responsibilities

• Build and ship LLM-powered applications and AI agent workflows.
• Design systems for reasoning, planning, memory, tool use and multi-step execution.
• Build reliable orchestration pipelines turning probabilistic model outputs into observable, safe actions.
• Outcomes: AI features reach production quickly and deliver measurable user impact.
• LLM-powered workflows are reliable, scalable, observable, and maintainable.

Skills

AI/ML fundamentals

Production-grade coding

Python programming

LLM/APIs

Agent frameworks

Distributed systems

PyTorch/JAX

Tools

OpenAI-compatible APIs

Vector databases

Retrieval systems

PyTorch

JAX

Orchestration systems

Job description

• Build and ship LLM-powered applications and AI 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.

Outcomes:

• AI features reach production quickly and deliver measurable user impact.
• LLM-powered workflows are reliable, scalable, observable, and maintainable.
• AI quality improves through systematic evaluation, experimentation, and iteration.

Qualifications:

• AI experience required with strong software engineering fundamentals.
• Hands-on experience with LLMs, generative AI, or agent-based systems.
• Ability to write clean, production-quality code and work across abstraction layers from model to product.
• Python, LLM APIs and model providers, including OpenAI-compatible APIs and open-weight models.
• Agent frameworks, orchestration systems, vector databases, and retrieval systems.
• Backend services, APIs, distributed systems, and PyTorch/JAX.