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Dir Applied AI Engineer

$167.70K - $268.33K/yr

... Part-time Full Time Salary Range Min - Max (USD) $167703 - $268325 Country United States Date 14 ... LLM orchestration and operationalization: Production patterns for integrating large language models ...

Stantec

Generative AI Developer

Phoenix, AZ · On-site

$104.80K - $152K/yr

... LLM-powered agents, multi-agent systems, retrieval-augmented generation (RAG), and intelligent ... Regular full-time and part-time employees (working at least 20 hours per week) have access to ...

Stantec

Generative AI Developer

Austin, TX · On-site

$104.80K - $152K/yr

... LLM-powered agents, multi-agent systems, retrieval-augmented generation (RAG), and intelligent ... Regular full-time and part-time employees (working at least 20 hours per week) have access to ...

Stantec

Generative AI Developer

Denver, CO · On-site

$104.80K - $152K/yr

... LLM-powered agents, multi-agent systems, retrieval-augmented generation (RAG), and intelligent ... Regular full-time and part-time employees (working at least 20 hours per week) have access to ...

Brain Gain Recruiting

Explainable AI Engineer

Palo Alto, CA · On-site +1

$114.90K - $157.30K/yr

Integrate Explainable AI with LLM tools for Agentic AI experience * Collaborate with the cloud ... You will have an opportunity to start as a contractor (preferred; full-time or part-time, at least ...

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How much do part time llm developer jobs pay per hour?

As of May 30, 2026, the average hourly pay for part time llm developer in the United States is $19.71, according to ZipRecruiter salary data. Most workers in this role earn between $13.70 and $19.95 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Part Time LLM Developer, and why are they important?

To thrive as a Part Time LLM Developer, you need strong programming skills (especially Python), a solid understanding of machine learning concepts, and experience with large language models (LLMs) or natural language processing. Familiarity with tools like TensorFlow, PyTorch, Hugging Face Transformers, and knowledge of cloud platforms or APIs is typically required. Effective problem-solving, time management, and clear communication are vital soft skills for collaborating remotely and balancing part-time workloads. These competencies are crucial for efficiently developing, deploying, and refining LLM-based applications while meeting project goals within limited hours.

What are some common challenges faced by part-time LLM developers, and how can they be managed effectively?

Part-time LLM developers often face challenges such as managing time efficiently to keep up with evolving AI frameworks and balancing project deadlines with limited working hours. Communication with full-time team members can also be tricky, as project updates and collaboration often happen outside their scheduled hours. To succeed, it's important to set clear expectations with your team, leverage asynchronous communication tools, and prioritize tasks that align with your expertise. Staying up-to-date with industry trends through self-study can also help bridge any knowledge gaps.

What is a Part Time LLM Developer?

A Part Time LLM Developer is a professional who works with large language models (LLMs), such as GPT or similar AI technologies, on a part-time basis. Their responsibilities can include designing, training, fine-tuning, and deploying LLMs for various applications like chatbots, content generation, or data analysis. Working part-time means they typically have flexible hours and may contribute to projects for multiple clients or companies. This role requires a strong foundation in programming, natural language processing, and machine learning concepts.
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Part Time Llm Developer jobs near you
Senior Prompt Engineer: LLM Migration & Optimization

Senior Prompt Engineer: LLM Migration & Optimization

Welo Data

Charleston, WV • Remote

$96.70K - $132.80K/yr

Part-time

Posted 12 days ago

Job description

Are you an expert at navigating the complex architecture of Large Language Models? Welo Data is seeking a Senior Prompt Engineer to lead the technical migration of template workflows into high-performance LLM autoraters.

This role is designed for a technical specialist who understands that "perfecting a prompt" is a rigorous engineering discipline. You will leverage advanced APG/APO tools and manual refinement to ensure our automated systems meet—and exceed—human crowd baselines in accuracy and nuance.

🚀 The Mission: Automated Quality at Scale
  • Architectural Migration: Take full ownership of the end-to-end technical migration of templates to LLM autoraters.
  • Optimization Leadership: Utilize Automatic Prompt Generation (APG) and supervise Automated Prompt Optimization (APO) tools to push model performance past plateaus and deadlocks.
  • Metrics-Driven Excellence: Continuously measure quality against "gold data" baselines, tracking precision, recall, and $F_1$ scores to justify launch readiness.
  • Edge-Case Engineering: Manually draft and refine complex prompts to overcome anti-patterns and architecture gaps that automated tools can't solve.
Project Details
  • Job Title: Senior Prompt Engineer (LLM Migration)
  • Schedule: Part-Time (Set your own hours within project milestones)
  • Location: 100% Remote (Must be based in the United States)
  • Employment Type: Freelance / Independent Contractor
Candidate Profile
  • Educational Foundation: Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Computational Linguistics, or a related analytical field.
  • Prompt Engineering Mastery: 4+ years of experience tuning LLMs for strict, structured outputs, complex classification, and few-shot learning.
  • Analytical Power: High proficiency in identifying error patterns and using SQL or data analytics tools to monitor performance.
  • Technical Agility: Fast learner capable of mastering proprietary internal tools and "Goose API" style interfaces with minimal oversight.
Preferred Technical Skills
  • Familiarity with shadowbot monitoring and disagreement tracking.
  • Experience in AI model evaluation and software engineering.
  • Deep understanding of semantics, logic, and Chain-of-Thought (CoT) prompting.
  • Proven ability to draft high-level Launch Certification Documentation.
Recruitment & Onboarding
  1. Technical Review: Submit your CV and portfolio of LLM optimization work.
  2. Prompt Assessment: Demonstrate your ability to navigate complex template clusters and APO deadlocks.
  3. Tooling Deep-Dive: Get access to our internal technical suite and migration workflows.
  4. Launch: Begin your part-time engagement and lead the shift to LLM-driven autorating.