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Genai Engineer Jobs in California (NOW HIRING)

2T Consulting

$104K - $137K/yr

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 ...

2T Consulting

$113K - $148K/yr

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 ...

2T Consulting

$124K - $163K/yr

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 ...

Danaher

Senior Staff GenAI Engineer

Sunnyvale, CA · On-site

$200K - $235K/yr

The Senior Staff GenAI Engineer leads the design, development, and deployment of scalable, reliable generative AI systems, driving agentic automation and integration to deliver highimpact ...

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Genai Engineer information

What is a GenAI engineer?

A GenAI Engineer is a professional who specializes in designing, developing, and deploying generative artificial intelligence (AI) models and applications. This role involves working with advanced machine learning techniques, such as large language models and generative adversarial networks, to create systems that can generate text, images, code, or other content. GenAI Engineers collaborate with data scientists, software engineers, and product teams to integrate AI capabilities into products and services, ensuring ethical use and scalability. They also stay updated on the latest developments in AI research to continually improve model performance and effectiveness.

What are the key skills and qualifications needed to thrive as a GenAI engineer, and why are they important?

To thrive as a GenAI Engineer, you need expertise in machine learning, deep learning, and programming languages such as Python, along with a solid understanding of generative models like GANs and transformers. Familiarity with frameworks such as TensorFlow or PyTorch, and experience with cloud platforms and MLOps tools, are highly valuable; advanced degrees or certifications in AI or data science are often preferred. Strong problem-solving, creativity, and communication skills help GenAI Engineers design innovative solutions and effectively collaborate with multidisciplinary teams. These skills ensure the development of robust, scalable generative AI systems that address complex real-world challenges.

What are some typical challenges a GenAI engineer faces when deploying AI models in production environments?

GenAI Engineers often encounter challenges such as ensuring model scalability, addressing bias in generated outputs, and maintaining performance consistency in real-world applications. Deploying generative AI models requires careful monitoring to prevent unexpected or inappropriate outputs, as well as efficient resource management to handle large-scale computations. Collaborating closely with data engineers, product managers, and ML operations teams is essential to streamline deployment pipelines and quickly resolve issues that arise in live environments.

What is the difference between Genai Engineer vs Data Scientist?

AspectGenai EngineerData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; experience with AI/ML frameworksDegree in Data Science, Statistics, or related fields; strong programming skills
Work EnvironmentDevelops AI models, fine-tunes generative AI systems, collaborates with AI teamsAnalyzes data, builds predictive models, interprets complex datasets
Employer & Industry UsageTech companies, AI startups, research labs focusing on generative AIFinance, healthcare, marketing, and tech firms analyzing data for insights

While both roles require strong technical skills and a background in data or AI, Genai Engineers focus on developing and deploying generative AI models, whereas Data Scientists analyze data to extract insights and build predictive models. The roles often overlap but serve different primary functions within AI and data-driven organizations.

What are popular job titles related to Genai Engineer jobs in California?

For Genai Engineer jobs in California, the most frequently searched job titles are:

What job categories do people searching Genai Engineer jobs in California look for?

The top searched job categories for Genai Engineer jobs in California are:

What cities in California are hiring for Genai Engineer jobs?

Cities in California with the most Genai Engineer job openings:

Genai Engineer jobs near you
Infographic showing various Genai Engineer job openings in California as of August 2026, with employment types broken down into 91% Full Time, 6% Part Time, and 3% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution.

GenAI Engineer - LLM Infrastructure & Inference Services

2T Consulting

San Jose, CA • On-site

$116K - $152K/yr

Full-time

Posted 8 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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