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Home Based Llm Engineer Jobs (NOW HIRING)

Ironflow AI

AI/LLM Integration Engineer

San Diego, CA

$110K - $148K/yr

As our AI/LLM engineer, you won't just write prompts -- you'll define how the system thinks. You'll ... materials based on available information. These tools assist our recruitment team but do not ...

Slalom Consulting

General Information

Boston, MA · On-site

Description and Requirements LLM Engineer Location: Remote Position Type: Full-Time, Project Based Consultant Start Date: 7/6/2026 Duration : 6 months About Us Slalom is a purpose-led, global ...

Hexaware Technologies, Inc

Python LLM Developer

Irving, TX · On-site

$48.25 - $66.50/hr

Role Python LLM Developer Location: Irving, TX ( day1 onsite, hybrid ) Python LLM Python LLM ... Know-how to integrate pre-trained AI models via APIs, e.g., cloud-based AI services. Data ...

Workato

Intern, AI Engineering

San Francisco, CA · On-site

$19.75 - $25.50/hr

You'll work on fundamental problems in LLM-based agentic systems and efficient AI infrastructure ... Mentor and collaborate with LLM engineers on implementation and deployment Requirements ...

Workato

Intern, AI Engineering

San Francisco, CA

$19.75 - $25.50/hr

You'll work on fundamental problems in LLM-based agentic systems and efficient AI infrastructure ... Mentor and collaborate with LLM engineers on implementation and deployment Requirements ...

TriOptus LLC

Gen AI / LLM Engineer (AWS)

Reston, VA · On-site

Client is seeking a highly skilled and motivated AI/ML Engineer to join client's team and drive the ... Monitor model performance and iterate based on feedback and metrics. * Stay current with ...

SoFi

Senior Staff AI Engineer

San Francisco, CA

$123K - $169K/yr

SoFi's Senior Staff AI Engineer is a hands-on AI engineering role in SoFi's growing independent ... Advanced LLM Orchestration: Architect and standardize the use of graph-based LLM orchestration ...

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Home Based Llm Engineer information

What is the difference between Home Based Llm Engineer vs Data Scientist?

AspectHome Based Llm EngineerData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; experience with machine learning modelsDegree in Data Science, Statistics, or related fields; proficiency in programming and analytics
Work EnvironmentRemote, focused on developing and fine-tuning language modelsRemote or on-site, analyzing data and building predictive models
Industry UsageAI companies, tech firms, research institutionsTech, finance, healthcare, and various industries requiring data analysis

Home Based Llm Engineers focus on developing and optimizing large language models remotely, while Data Scientists analyze data to generate insights. Both roles require strong technical skills and often work in similar industries, but their core responsibilities differ in model development versus data analysis.

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Home Based Llm Engineer jobs near you
Infographic showing various Home Based Llm Engineer job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 79% Full Time, 14% Part Time, and 6% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution.
GenAI Engineer - Remote

GenAI Engineer - Remote

MM International

San Francisco, CA • Remote

Contractor

Posted 24 days ago

Job description

GenAI/LLM Engineer (NLP, TensorFlow, PyTorch SME)

San Francisco, Bay Area, CA

Duration: Six months may extend to 12 months

Must be in the Greater Bay area – or in California

Domain: utilities

GenAI/LLM Engineer (NLP, TensorFlow, PyTorch SME)

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's 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's 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