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Remote Llm Training information

What is remote LLM training?

Remote LLM training refers to the process of training large language models (LLMs), such as GPT or similar AI models, on distributed computing resources that are accessed remotely. This allows data scientists and AI engineers to leverage powerful hardware, like GPUs or TPUs, which may not be available locally. Remote LLM training is commonly used to handle the massive computational requirements of modern AI models and enables collaboration among teams in different locations. It also provides scalability, flexibility, and cost-effectiveness for organizations working on advanced AI projects.

What skills and qualifications are needed for remote LLM training?

To excel in Remote LLM Training, you need a strong background in machine learning, natural language processing, and computer science, often demonstrated by a relevant degree or industry experience. Familiarity with frameworks like PyTorch or TensorFlow, experience with large-scale data management, and knowledge of distributed computing systems are typically required. Strong problem-solving skills, effective communication, and the ability to work independently are vital soft skills in this remote, collaborative environment. These competencies ensure efficient model training, high-quality output, and seamless teamwork across distributed teams.

What are common challenges in remote LLM training, and how can they be addressed?

Professionals in remote LLM (Large Language Model) training roles often face challenges such as managing distributed team communication, ensuring data privacy, and handling large-scale computational resources. Staying organized with asynchronous collaboration tools and maintaining clear documentation can help streamline teamwork. Additionally, understanding cloud-based infrastructure and adhering to strict data security protocols are essential for handling sensitive datasets. Regular check-ins and knowledge-sharing sessions also foster a supportive and productive remote work environment.

What is the difference between Remote Llm Training vs Data Scientist?

AspectRemote Llm TrainingData Scientist
Required CredentialsKnowledge of NLP, machine learning, programming skillsStatistics, programming, domain expertise
Work EnvironmentRemote, collaborative teams, AI/ML companiesRemote or on-site, diverse industries
Industry UsageAI development, NLP projectsData analysis, predictive modeling

Remote Llm Training focuses on developing and fine-tuning large language models, requiring expertise in NLP and machine learning. Data Scientists analyze data to extract insights and build models across various industries. While both roles involve programming and data skills, Remote Llm Training is specialized in AI model development, whereas Data Scientists work on broader data analysis tasks.

What are the most commonly searched types of Llm Training jobs in California?

The most popular types of Llm Training jobs in California are:

What are popular job titles related to Remote Llm Training jobs in California?

For Remote Llm Training jobs in California, the most frequently searched job titles are:

What job categories do people searching Remote Llm Training jobs in California look for?

The top searched job categories for Remote Llm Training jobs in California are:

What cities in California are hiring for Remote Llm Training jobs?

Cities in California with the most Remote Llm Training job openings:

Remote Llm Training jobs near you
Infographic showing various Remote Llm Training job openings in California as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 13% Part Time, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution.

Senior Generative AI Engineer (Google Gemini & Vertex AI) - Remote

DivIHN

San Diego, CA • On-site, Remote

$110K - $152K/yr

Contractor

Posted 17 days ago

Key responsibilities

  • Develop asynchronous microservices using FastAPI with SSE or WebSockets to stream real-time LLM responses to front-end UIs.

  • Design, develop, and implement vector database infrastructure to capture information from diverse engineering sources.

  • Build and optimize data extraction and structuring pipelines for multi-modal data from unstructured documents.

Job description

For further inquiries about this opportunity, please contact one of our Talent Specialists, Justeen at (224)-394-4903
Title: Senior Generative AI Engineer (Google Gemini & Vertex AI) - Remote
Duration: 6 Months with possible for extension
Location: Remote
Candidates must be available to work West Coast (PST) hours.
Only W2 candidates are eligible for this position. Third-party or C2C candidates will not be considered.
Day to Day Responsibilities
  • RAG & Prompt Engineering: Craft and refine effective prompts for RAG, grounding, and context tuning to achieve optimal AI performance in product development.
  • High-Performance API Engineering: Develop asynchronous microservices (FastAPI) using Server-Sent Events (SSE) or WebSockets to stream real-time LLM responses to front-end UIs without backend timeouts.
  • Vector Database Infrastructure: Design, develop, and implement robust Vector Databases using LLMs and modern retrieval technologies to capture information from diverse engineering sources (PDFs, design docs, regulatory guidelines).
  • Data Extraction & Structuring Pipelines: Build and optimize pipelines to extract and structure multi-modal data (tables, text, images) from unstructured documents for LLM training, grounding, and runtime query execution.
  • LLM Fine-Tuning & Training: Fine-tune and train generative AI models using client's engineering data and domain knowledge to create high-accuracy, domain-specific models.
  • GenAI Application & Tool Development: Design and implement scalable backend APIs (FastAPI/REST) and UI integration interfaces so internal engineers can query knowledge bases and analyze data.
  • Automated Requirements Generation: Develop backend functionalities to automatically generate technical requirements from design documents, user stories, and system specification files.
  • Documentation & Knowledge Transfer: Thoroughly document architecture, code, REST endpoints, and model training procedures to enable seamless knowledge transfer to client's internal teams.
  • Cross-Functional Collaboration: Partner closely with Subject Matter Experts (SMEs), System Engineers, and V&V Test teams to optimize AI-powered workflows.
  • AI Guardrails, MLOps & Cost Governance: Implement hallucination checks, PII masking, and guardrails (e.g., NeMo Guardrails) for medical device context. Track token usage, latency, and costs using LangSmith or Vertex AI monitoring.
  • Collaborate closely with engineers and cross-functional teams to design and develop AI solutions.
  • Develop and integrate Generative AI applications and APIs.
  • Work with large datasets, document processing, and data pipelines.
  • Participate in problem-solving, solution design, and technical discussions.
  • Communicate effectively with team members and stakeholders.

Must-Have Qualifications :
  • Relevant Experience & STEM Foundation: 4+ years of professional software/ML engineering experience, with a dedicated AI/ML focus in the last 1-2 years.
  • Google Gemini / Vertex AI (Non-negotiable): Hands-on experience with the Gemini model family and Vertex AI, including deployment, grounding, and integration into production AI services. Hands-on experience with containerization (Docker) and deploying services via Cloud Run or GKE (Kubernetes).
  • Languages & AI Libraries: Proficiency in Python and modern ML/AI frameworks (PyTorch, LangChain, LangSmith) for building autonomous LLM agents, tools, and RAG pipelines.
  • Agent Building & Tool Calling: Proven experience building AI/LLM agents and tool-calling systems in Python against unstructured, multi-source data.
  • Context Engineering & RAG: Expertise in RAG pipelines, prompt engineering, context tuning, grounding, and Vector Databases (e.g., Milvus, Postgres/Pgvector). Clear understanding of advanced RAG architecture including Hybrid Search (Vector + Keyword), Re-ranking models, and semantic caching.
  • Unstructured Data Handling (Non-negotiable): Demonstrated ability to ingest, clean, extract, and structure text, tables, and images from unstructured documents (PDFs, design docs, regulatory files) for LLM training and usage.

Role Information
  • Generative AI Engineer role.
  • Experience in API development.
  • Hands-on experience with Google Gemini and Vertex AI.
  • Knowledge of Agentic AI frameworks and architectures.
  • Strong Python development skills, including experience with LangChain and related AI frameworks.
  • Open to candidates from any industry domain.

Required Skills :
  1. Full Stack Engineering - Building applications for AI-powered services (Backend APIs + Front-End Integration).
  2. Generative AI & LLM Platforms - 2+ years building RAG pipelines & LLM apps using Python, LangChain, and LangSmith.
  3. Agent Building & Unstructured Data - Building AI agents/tool-calling systems in Python and handling unstructured, multi-source data extraction (PDFs, docs).
  4. Strong hands-on experience in Artificial Intelligence and Generative AI
  5. Python programming expertise.
  6. Experience with LangChain and Agentic AI frameworks.
  7. Understanding data handling to pull data from PDFs, design docs, regulatory files

Preferred Skills :
  1. Direct experience architecting and serving custom REST APIs.
  2. Experience in regulated / compliance-sensitive domains (e.g., healthcare / medical device guidelines like FDA, ISO 13485).
  3. Experience integrating multiple LLM APIs beyond a single provider (OpenAI, Bedrock, Claude, or similar).

Additional Preferred Skills
  • Direct experience designing and deploying high-throughput REST APIs (e.g., FastAPI/Flask).
  • Familiarity with medical device development regulations and compliance (e.g., FDA guidelines, ISO 13485).
  • Experience integrating multiple LLM APIs beyond a single vendor (e.g., OpenAI, AWS Bedrock, Anthropic Claude).
  • Front-end development/integration experience for UI design (e.g., Streamlit, Gradio, React/Next.js integration).

Education Requirements:
  • Minimum Bachelors in Software/Computer/IT/Systems/Biomedical Engineering + 3 years

Required Testing:
  • Technical evaluation of Python proficiency, RAG architecture concepts, and API/Agent design.

Software Skills Required:
  • Languages: Python (AsyncIO, OOP), SQL.
  • AI & Agent Frameworks: PyTorch, LangChain, LangSmith, Vertex AI SDK.
  • API & Web Frameworks: FastAPI, Flask, REST APIs, Server-Sent Events (SSE).
  • Databases & Search: Milvus, Pgvector, Qdrant, Redis (caching).
  • Front-End Integration: Streamlit, Gradio, basic React/Next.js.
  • Testing & Guardrails: pytest, JUnit, LangSmith evaluation, NeMo Guardrails.
  • DevOps & Cloud: Docker, GCP (Vertex AI, Cloud Run, GKE), Git.

Required Certifications:
  • Professional certifications specific to AI/ML (e.g., Certified AI Professional / CAIP, Google Cloud ML Engineer) considered a plus.

Interview
  • Number of Interviews: 1,
  • Web Conference (Zoom/ TEAMs)
  • Live problem-solving and technical exercise during the interview.

Apply