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Part Time Llm Developer Jobs in Wakefield, MA (NOW HIRING)

Solution Architect

Lexington, MA · On-site +1

$70.75 - $93.25/hr

  • Medical

  • Life

  • Retirement

  • PTO

... into engineering workflows or production environments, including surfacing LLM-backed tooling to ... Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible ...

Sr. Tax Manager (REMOTE)

Waltham, MA · On-site +1

$134K - $167K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Revvity | About Us Revvity is a developer and provider of end-to-end solutions designed to help ... MST or JD/LLM preferred. * Strong technical knowledge of U.S. international tax regulations ...

Sr. Tax Manager (REMOTE)

Boston, MA · On-site +1

$134K - $167K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Revvity | About Us Revvity is a developer and provider of end-to-end solutions designed to help ... MST or JD/LLM preferred. * Strong technical knowledge of U.S. international tax regulations ...

Part Time Llm Developer information

See Wakefield, MA salary details

$8

$20

$42

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

As of Aug 19, 2026, the average hourly pay for part time llm developer in Wakefield, MA is $20.77, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $21.06 per hour, depending on experience, location, and employer.

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.

What are the key skills and qualifications needed to thrive as a part time LLM developer?

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 job categories do people searching Part Time Llm Developer jobs in Wakefield, MA look for?

The top searched job categories for Part Time Llm Developer jobs in Wakefield, MA are:

What cities near Wakefield, MA are hiring for Part Time Llm Developer jobs?

Cities near Wakefield, MA with the most Part Time Llm Developer job openings:

Infographic showing various Part Time Llm Developer job openings in Wakefield, MA as of August 2026, with employment types broken down into 79% Full Time, 3% Part Time, and 18% Contract. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution, with an average salary of $43,209 per year, or $20.8 per hour.

Temporary Micro-Credential Grader - Industry-Focused Prompt Engineering for ROI-Driven Results

Brandeis University

Waltham, MA • On-site

$25 - $30/hr

Part-time

Re-posted 22 days ago


Job description

Location: Fully remote (U.S.-based applicants only, no visa sponsorships)
Division: Rabb School of Continuing Studies, Brandeis University
Type: Part-Time, 4 months, varying hours, no more than 25 hours per week
Compensation: Hourly $25-$30
Reports to: Assistant Dean of Education and Learning Innovation
Brandeis University's Rabb School of Continuing Studies is seeking a detail-oriented professional with expertise in applied AI to serve as a Micro-Credential Grader for the online asynchronous credential, Industry-Focused Prompt Engineering for ROI-Driven Results.
In this fully remote, short-term hourly position, you'll evaluate learner submissions that demonstrate practical mastery in designing, testing, and refining prompts for large language models (LLMs) to support measurable organizational objectives. This credential equips professionals with the ability to align prompt strategies with business goals, evaluate platform performance, and quantify the ROI impact of AI-driven solutions. As a grader, you'll apply structured rubrics to assess strategic thinking, prompt engineering fluency, and outcome-based reasoning.
This role offers a unique opportunity to contribute to a cutting-edge, workforce-aligned credential that bridges AI innovation with business impact.
What You Will Do
  • Evaluate learner submissions that include prompt design portfolios, platform performance analyses, and ROI impact assessments tied to real-world business use cases.
  • Apply structured rubrics to assess mastery of skills such as LLM prompt iteration, use-case alignment, performance benchmarking, and ROI quantification.
  • Review learner reflections on prompt strategy effectiveness, ethical considerations, and organizational integration.
  • Participate in calibration exercises with fellow graders (if needed) to ensure consistency in evaluating strategic, technical, and business-oriented artifacts.
  • Maintain confidentiality and objectivity throughout the grading process.

What You Bring
  • Bachelor's degree required; Master's degree preferred in Computer Science, Data Science, Business Analytics, or related disciplines.
  • Subject-matter expertise in prompt engineering, LLM capabilities, and AI deployment for business outcomes.
  • Experience in academic assessment, workforce development, or digital learning preferred.
  • Familiarity with learning management systems (Moodle preferred), online credentialing platforms, and collaborative grading workflows.
  • Professional, learner-centered approach with a commitment to academic integrity and continuous improvement.
  • Proficient in rubric-based assessment and competency validation, especially for strategic and project-based submissions.
  • Strong attention to detail and ability to maintain consistency across diverse learner artifacts.
  • Excellent written communication skills for delivering constructive, learner-focused feedback.
  • Comfortable working in asynchronous learning environments and using digital platforms.
  • Adaptability in managing multiple grading tasks within deadlines.

Pay Range Disclosure
The University's pay ranges represent a good faith estimate of what Brandeis reasonably expects to pay for a position at the time of posting. The pay offered to a selected candidate during hiring will be based on factors such as (but not limited to) the scope and responsibilities of the position, the candidate's work experience and education/training, internal peer equity, and applicable legal requirements.
Equal Opportunity Statement
Brandeis University is an equal opportunity employer which does not discriminate against any applicant or employee on the basis of race, color, ancestry, religious creed, gender identity and expression, national or ethnic origin, sex, sexual orientation, pregnancy, age, genetic information, disability, caste, military or veteran status or any other category protected by law (also known as membership in a "protected class").