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Genai Developers Jobs (NOW HIRING)

2T Consulting

$121K - $159K/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 ...

Brooksource

GenAI Developer

Mclean, VA · On-site

$65 - $75/hr

GenAI Developer Location: DMV Area (Washington, DC, Maryland, Virginia) Hybrid Role - 1-2 days/wk ... Collaborate with developers, AI/ML engineers, product teams, and other technical stakeholders to ...

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 ...

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Genai Developers information

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$35K

$71K

$230.5K

How much do genai developers jobs pay per year?

As of Sep 14, 2026, the average yearly pay for genai developers in the United States is $71,017.00, according to ZipRecruiter salary data. Most workers in this role earn between $42,000.00 and $60,000.00 per year, depending on experience, location, and employer.

What is the difference between Genai Developers vs Machine Learning Engineers?

AspectGenai DevelopersMachine Learning Engineers
Required CredentialsBachelor's in CS, AI, or related; experience with AI frameworksBachelor's or higher in CS, Data Science, or related; strong math background
Work EnvironmentTech companies, startups, AI-focused teamsResearch labs, tech firms, AI product teams
Industry UsageDeveloping generative AI applications, chatbots, content creationBuilding predictive models, data analysis, machine learning systems

Genai Developers focus on creating applications using generative AI models, often working with NLP and content generation tools. Machine Learning Engineers develop and optimize machine learning algorithms and models across various domains. While both roles require AI knowledge, Genai Developers specialize in generative AI applications, whereas Machine Learning Engineers have a broader scope in machine learning systems.

What are popular job titles related to Genai Developers jobs?

For Genai Developers jobs, the most frequently searched job titles are:

Infographic showing various Genai Developers job openings in the United States as of September 2026, with employment types broken down into 90% Full Time, 6% Part Time, and 4% Contract. Highlights an 73% Physical, 5% Hybrid, and 22% Remote job distribution, with an average salary of $71,017 per year, or $34.1 per hour.

GenAI Engineer - LLM Infrastructure & Inference Services

Newark, CA • On-site

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

$120K - $158K/yr

Full-time

Posted 17 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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