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Python Llm Jobs in Los Altos, CA (NOW HIRING)

HCL Global Systems

Python+ React FDE with AI/ML

Sunnyvale, CA · On-site

$59.75 - $82.50/hr

Candidate should be able to build production-grade backend services in Python and modern UIs in ... LLM APIs (Claude, GPT-4, Gemini) and agentic AI workflows into customer-facing products • ...

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How much do python llm jobs pay per hour?

As of Sep 12, 2026, the average hourly pay for python llm in Los Altos, CA is $69.43, according to ZipRecruiter salary data. Most workers in this role earn between $57.21 and $78.85 per hour, depending on experience, location, and employer.

What is a Python LLM?

A Python LLM job involves working with Large Language Models (LLMs) using Python to develop, fine-tune, and deploy AI models. Responsibilities may include data preprocessing, prompt engineering, model optimization, and integration with applications. Professionals in this role often work with frameworks like TensorFlow, PyTorch, or Hugging Face Transformers. They may also contribute to improving model efficiency, reducing bias, and ensuring ethical AI usage.

What are the key skills and qualifications needed to thrive in the Python LLM position, and why are they important?

To excel as a Python LLM (Large Language Model) Engineer, you need strong skills in Python programming, machine learning, and natural language processing, typically supported by a degree in computer science or a related field. Proficiency with libraries such as TensorFlow, PyTorch, Hugging Face Transformers, and experience with model deployment platforms are often essential, alongside certifications in AI or data science. Effective communication, problem-solving abilities, and collaboration are important soft skills for working in interdisciplinary teams and delivering results in dynamic environments. These skills ensure the development, fine-tuning, and deployment of advanced language models that meet both technical and business objectives.

What are some common challenges faced by Python LLM engineers in their daily work?

Python LLM Engineers often encounter challenges related to optimizing model performance, managing large datasets, and adapting models to specific business needs. Working with large-scale language models requires balancing computational resource limitations with the need for high accuracy and efficiency. Collaboration with data scientists, product managers, and DevOps engineers is routine to ensure seamless model integration and deployment. Staying updated on the latest advancements in NLP and continuously improving models based on user feedback are also important aspects of the role.

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Python Llm jobs near you
Infographic showing various Python Llm job openings in Los Altos, CA as of August 2026, with employment types broken down into 1% Internship, 86% Full Time, 8% Part Time, and 5% Contract. Highlights an 79% Physical, 5% Hybrid, and 16% Remote job distribution, with an average salary of $144,405 per year, or $69.4 per hour.

GenAI Engineer - LLM Infrastructure & Inference Services

Los Altos, CA • On-site

2T Consulting
IT Services • 51 - 200 employees
Learn more about 2T Consultingexternal

$127K - $167K/yr

Full-time

Posted 14 days ago

Job description

We are looking for a GenAI Engineer with strong expertise in LLM infrastructure, model deployment, and high-performance inference services. The ideal candidate will build and manage scalable enterprise GenAI platforms across GPU infrastructure and cloud environments.

Key Responsibilities
  • Deploy, host, and manage Large Language Models (LLMs) on GPU infrastructure for production environments.
  • Build scalable, high-performance inference services using vLLM, TensorRT-LLM, Triton Inference Server, and Ray Serve.
  • Optimize model serving for latency, throughput, GPU utilization, and cost efficiency.
  • Develop AI platform services and APIs using Python, FastAPI, Microservices, and Kubernetes.
  • Implement RAG pipelines, vector databases, and agentic AI frameworks such as LangChain and LangGraph.
  • Manage GPU infrastructure, containerization, and cloud deployments across AWS, Azure, or GCP.
  • Establish MLOps/LLMOps practices including CI/CD, model deployment, monitoring, observability, and governance.
  • Perform performance tuning, benchmarking, capacity planning, and production support for enterprise GenAI platforms.
  • Collaborate with architects, data scientists, and product teams to deliver scalable, secure, and reliable AI solutions.
Core Technologies
  • LLM: vLLM, TensorRT-LLM, Triton Inference Server, Ray Serve
  • AI/GenAI: RAG, LangChain, LangGraph, Vector Databases
  • Development: Python, FastAPI, Microservices
  • Infrastructure: Kubernetes, Docker, GPU Infrastructure
  • Cloud: AWS, Azure, GCP
  • MLOps/LLMOps: CI/CD, Monitoring, Observability, Model Deployment, Governance

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