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Remote Entry Level Prompt Engineer Jobs (NOW HIRING)

Tutorperini

Project Engineer Entry-Level

Milwaukee, WI · On-site +1

$66K - $87K/yr

Hybrid or remote work options are not available. NO STAFFING AGENCY CANDIDATES WILL BE CONSIDERED ... As a Entry-Level Project Engineer at Lunda Construction, reporting to Regional Manager, you will ...

Tutorperini

Project Engineer Entry-Level

Black River Falls, WI · On-site +1

$63K - $84K/yr

Hybrid or remote work options are not available. NO STAFFING AGENCY CANDIDATES WILL BE CONSIDERED ... As a Entry-Level Project Engineer at Lunda Construction, reporting to Regional Manager, you will ...

Pyrovio

Copilot Developer/AI Engineer

... prompt engineering: crafting effective prompts, skills, and grounding/context strategies • ... remote-first environment while maintaining strong coworker relationships. Preferred : • ...

evrecruit.io

AI Interfaces Engineer

San Francisco, CA · Remote

Bay Area, remote-flexible Compensation: Competitive Base / Equity mix As an AI Interfaces Engineer ... This is a primarily hands-on role that blends prompt engineering, full-stack engineering, and AI ...

Interviews Chat

GenAI Engineer

Manhattan, NY · Remote

$90K - $150K/yr

Build and iterate on prompt systems, RAG pipelines, and context assembly (resume, , live transcript ... What We Offer: * 100% remote work with flexible hours. * Competitive salary (depending on ...

Lorven Technologies

Senior/Lead Data Engineer with AI

$108K - $147K/yr

Senior/Lead Data Engineer with AI Location: 100% remote Contract length - 1+ year Required start ... Understanding of prompt engineering, system messages, and model parameter tuning for optimal AI ...

GNY Insurance Companies

Associate AI Engineer

Edison, NJ · Remote

$69K - $124K/yr

Description Remote position with the ability to travel to our NJ and NY locations up to 25% of the ... LLMs, prompt engineering, and RAG architectures * Vector databases and semantic search * Agent ...

Pyrovio

Copilot Developer/AI Engineer

Akron, OH · Remote

Collaborate in prompt engineering: crafting effective prompts, skills, and grounding/context ... Ability to work independently in a remote-first environment while maintaining strong coworker ...

TEKsystems

Principal AI Engineer

Raleigh, NC · On-site +1

$75 - $100/hr

... prompt engineering frameworks, guardrails, and automated evaluation suites for agent reliability ... remote position. Application Deadline This position is anticipated to close on Jun 12, 2026. About ...

tyfonecom

AI Forward Engineer

Portland, OR · Remote

This is a remote position. Title: AI Forward Engineer Location: Remote, USA (Preferable in EDT ... You will own AI features end-to-end -- from prompt design and model selection through deployment ...

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How much do remote entry level prompt engineer jobs pay per year?

As of Jun 9, 2026, the average yearly pay for remote entry level prompt engineer in the United States is $69,362.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,500.00 and $78,500.00 per year, depending on experience, location, and employer.

What are some common challenges faced by remote entry level prompt engineers, and how can these be managed effectively?

Remote entry level prompt engineers often encounter challenges like ambiguous instructions, rapidly changing project requirements, and limited opportunities for immediate feedback. To manage these, it's helpful to proactively communicate with team members, ask clarifying questions, and make use of collaborative tools such as Slack or project management platforms. Regular check-ins and seeking mentorship from experienced engineers can also help address uncertainties and accelerate your learning curve. Building strong documentation habits and participating in team discussions will further support your growth and integration within the remote team.

What is a Remote Entry Level Prompt Engineer?

A Remote Entry Level Prompt Engineer is a professional who creates, tests, and refines prompts for AI language models, such as ChatGPT, while working from a remote location. Their main responsibility is to design clear and effective instructions that guide AI systems to generate accurate, relevant, and useful responses. This role typically requires analytical thinking, strong communication skills, and an interest in artificial intelligence, but does not always demand advanced technical expertise. Entry-level prompt engineers often collaborate with other team members to optimize prompts and improve model outputs. Working remotely allows these professionals to perform their duties from anywhere with an internet connection.

What are the key skills and qualifications needed to thrive as a Remote Entry Level Prompt Engineer, and why are they important?

To thrive as a Remote Entry Level Prompt Engineer, you need a solid understanding of language models, basic programming knowledge (often in Python), and strong problem-solving abilities, typically supported by a relevant degree or demonstrated experience. Familiarity with AI platforms like OpenAI's GPT, prompt engineering tools, and version control systems such as Git is commonly required. Creativity, attention to detail, and clear written communication are crucial soft skills for crafting effective prompts and collaborating remotely. These skills ensure the development of accurate, reliable AI outputs and foster productive teamwork in a distributed environment.
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Remote Entry Level Prompt Engineer jobs near you

GenAI / LLM Engineer - Remote (should be able to work on PST time zones)

Rootshell Enterprise Technologies, Inc.

Remote

Other

Posted 21 days ago

Job description

GenAI/LLM Engineer
Remote (should be able to work on PST time zones)
Prefers local to bay area.
Implementing GenAI requires specialized expertise in large language models. Traditional data scientists often haven't had the opportunity to dive deep into the practical intricacies of LLMs-particularly advanced fine-tuning techniques, model compression strategies, memory optimization approaches, and specialized training workflows. This role requires a hands-on deep learning practitioner comfortable with modern frameworks and libraries specific to LLM development.
  • Enables domain-specific fine-tuning of models to Client unique utility context
  • Improves model performance while reducing computational costs through advanced optimization techniques
  • Creates Client-specific AI capabilities that address our unique operational challenges
  • Enables the CoE to move beyond generic AI tools to customized solutions that deliver higher business value

Key Responsibilities:
  • Implement and optimize advanced fine-tuning approaches (LoRA, PEFT, QLoRA) to adapt foundation models to Client domain
  • Develop systematic prompt engineering methodologies specific to utility operations, regulatory compliance, and technical documentation
  • Create reusable prompt templates and libraries to standardize interactions across multiple LLM applications and use cases
  • Implement prompt testing frameworks to quantitatively evaluate and iteratively improve prompt effectiveness
  • Establish prompt versioning systems and governance to maintain consistency and quality across applications
  • Apply model customization techniques like knowledge distillation, quantization, and pruning to reduce memory footprint and inference costs
  • Tackle memory constraints using techniques such as sharded data parallelism, GPU offloading, or CPU+GPU hybrid approaches
  • Build robust retrieval-augmented generation (RAG) pipelines with vector databases, embedding pipelines, and optimized chunking strategies
  • Design advanced prompting strategies including chain-of-thought reasoning, conversation orchestration, and agent-based approaches
  • Collaborate with the MLOps engineer to ensure models are efficiently deployed, monitored, and retrained as needed

Expected Skillset:
  • Deep Learning & NLP: Proficiency with PyTorch/TensorFlow, Hugging Face Transformers, DSPy, and advanced LLM training techniques
  • GPU/Hardware Knowledge: Experience with multi-GPU training, memory optimization, and parallelization strategies
  • LLMOps: Familiarity with workflows for maintaining LLM-based applications in production and monitoring model performance
  • Technical Adaptability: Ability to interpret research papers and implement emerging techniques (without necessarily requiring PhD-level mathematics)
  • Domain Adaptation: Skills in creating data pipelines for fine-tuning models with utility-specific content

Apply