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Llm Developer Jobs in Reston, VA (NOW HIRING)

CogniSoft Technologies

Python Developer with AI/LLM

Mclean, VA · On-site

$51.50 - $71/hr

Python Developer with AI/LLM Location: Mc Lean, VA Developer-Full Stack Specialist Required Experience & Education • 5+ years of professional software development experience. • 1+ years of hands ...

Steampunk

AI Developer

Mclean, VA

$140K - $190K/yr

Develop end-to-end AI solutions including LLM-powered applications, predictive ML models, multi ... Mentor junior developers, conduct code reviews, and support engineering excellence across multi ...

Steampunk

AI Developer

Mclean, VA · On-site

$140K - $190K/yr

Develop end-to-end AI solutions including LLM-powered applications, predictive ML models, multi ... Mentor junior developers, conduct code reviews, and support engineering excellence across multi ...

Steampunk

AI Developer

Mclean, VA

$140K - $190K/yr

Develop end-to-end AI solutions including LLM-powered applications, predictive ML models, multi ... Mentor junior developers, conduct code reviews, and support engineering excellence across multi ...

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All JobsLlm Developer JobsLlm Developer Jobs in Reston, VA

Llm Developer information

See Reston, VA salary details

$26

$52

$82

How much do llm developer jobs pay per hour?

As of Jul 25, 2026, the average hourly pay for llm developer in Reston, VA is $52.19, according to ZipRecruiter salary data. Most workers in this role earn between $41.01 and $63.27 per hour, depending on experience, location, and employer.

What is the salary of LLM developer?

The salary of an LLM developer typically ranges from $80,000 to $150,000 annually, depending on experience, location, and the complexity of projects. Skilled developers with expertise in machine learning, natural language processing, and relevant tools like Python and TensorFlow tend to earn higher salaries.

What is a $900000 AI job?

A $900,000 AI job typically refers to high-level roles such as senior AI researchers or machine learning executives, often involving advanced skills in deep learning, large language models, and extensive experience. These positions may include leadership responsibilities, require specialized certifications, and offer compensation packages that include salary, bonuses, and stock options. Such roles are usually found in top tech companies or AI-focused organizations and demand a strong track record of innovation and technical expertise.

What does an LLM Developer do?

An LLM Developer designs, fine-tunes, and implements large language models (LLMs) for various applications, such as chatbots, content generation, and AI-driven tools. They work with machine learning frameworks, optimize model performance, and ensure efficient deployment. This role requires expertise in natural language processing (NLP), deep learning, and programming languages like Python.

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

To excel as an LLM Developer, you need strong expertise in natural language processing (NLP), deep learning frameworks, and programming languages such as Python, typically supported by a degree in computer science or a related field. Familiarity with machine learning libraries (like TensorFlow or PyTorch), cloud computing platforms, and experience with prompt engineering or fine-tuning large language models is crucial. Excellent problem-solving abilities, collaboration, and effective communication skills help you design solutions and work efficiently within multidisciplinary teams. These qualifications are essential for successfully building, deploying, and optimizing large language models that drive impactful AI applications.

What engineers make $500,000?

Senior machine learning engineers, including those developing large language models (LLMs), can earn $500,000 or more annually, especially with extensive experience, advanced skills in deep learning, and work at top tech companies. High compensation often includes base salary, bonuses, and stock options, particularly in competitive markets or leadership roles.

What are the typical daily tasks and responsibilities of an LLM Developer?

As an LLM Developer, your daily responsibilities often include designing, fine-tuning, and evaluating large language models to meet specific application needs. You may work on tasks such as data preprocessing, model training, performance benchmarking, and error analysis, frequently collaborating with data scientists, research engineers, and product managers. Keeping up to date with the latest advancements in NLP and integrating new techniques into production models is also a key part of the role. These tasks are usually performed in a team-oriented environment where clear communication and iterative experimentation are highly valued.

What are LLM developers?

LLM developers are software engineers who design, build, and optimize large language models used in artificial intelligence applications. They typically work with machine learning frameworks, programming languages like Python, and tools such as TensorFlow or PyTorch to develop models for tasks like natural language processing and understanding.
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Llm Developer jobs near you
Artificial Intelligence Engineer LLM

Artificial Intelligence Engineer LLM

The Josef Group Inc.

Chantilly, VA • On-site

$110K - $144K/yr

Other

This job post has expired 2 days ago. Applications are no longer accepted.

Job description

LLM Engineer

Top Secret Clearance (TS/SCI) is required

We are seeking an experienced LLMOps Engineer to deploy, operate, and optimize self-hosted Large Language Models (LLMs) in production.

Understanding of how modern LLMs work, including transformer architectures, inference optimization, quantization, and fine-tuning, and hands-on experience managing production AI infrastructure.

  • Deploy, manage, and optimize self-hosted LLMs using vLLM on GPU infrastructure.
  • Configure multi-GPU deployments, including tensor parallelism, PagedAttention, continuous batching, and KV cache optimization.
  • Deploy and serve open-weight models such as Llama, Mistral, Qwen, and other Hugging Face models.
  • Optimize inference performance through quantization (AWQ, GPTQ, FP8) and benchmark latency, throughput, and GPU utilization.
  • Build and maintain scalable Kubernetes-based AI infrastructure.
  • Monitor model health, performance, and production reliability.
  • Manage model versioning, deployments, rollbacks, and production releases.
  • Implement CI/CD pipelines, Infrastructure-as-Code, observability, and incident response for AI platforms.
  • Strong understanding of LLM architecture, transformer models, tokenization, inference, and fine-tuning.
  • Hands-on experience deploying and operating vLLM in production.

Kubernetes, Docker, GPU clusters, and cloud platforms (AWS, Azure, or GCP).

  • Open-source LLMs (Llama, Mistral, Qwen, etc.).
  • Experience optimizing inference using tensor parallelism, PagedAttention, continuous batching, KV cache management, and quantization.
  • Strong Python and Linux administration skills.
  • Prometheus and Grafana.
  • Experience with Infrastructure-as-Code (Terraform, Helm, Ansible) and CI/CD tools.

Preferred Skills

  • Hugging Face
  • LoRA fine-tuning
  • RAG architectures
  • Vector databases
  • TensorRT-LLM
  • Triton Inference Server
  • Ray Serve
  • NVIDIA CUDA, A100, and H100 GPUs
  • LangChain
  • DevSecOps and Site Reliability Engineering (SRE)

Keywords: SGLang LLMOps, MLOps, vLLM, Kubernetes, Docker, Python, Hugging Face, Llama, Mistral, Qwen, TensorRT-LLM, Triton, Ray Serve, CUDA, NVIDIA GPU, Terraform, Helm, AWS, Azure, GCP, Prometheus, Grafana, CI/CD, Infrastructure as Code, RAG, Vector Database.

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