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Vllm Jobs in Tennessee (NOW HIRING)

ITR

Kubernetes Platform Engineer

Oak Ridge, TN

KServe, Kubeflow, vLLM, NVidia Enterprise AI, AMD Silo AI, ClearML, MLFlow * Experience using HPC hardware for Kubernetes - e.g. RDMA, DPUs, Infiniband, many-core CPUs * Experience with declarative ...

Cadre5

Senior Platform Engineer

Knoxville, TN · On-site

$93K - $127K/yr

KServe, Kubeflow, vLLM, NVidia Enterprise AI, AMD Silo AI, ClearML, MLFlow * Experience using HPC hardware for Kubernetes - e.g. RDMA, DPUs, Infiniband, many-core CPUs * Experience with declarative ...

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Vllm information

What is a vLLM?

VLLM stands for 'Virtual Large Language Model.' In the context of AI development, VLLM professionals work with optimized inference engines for large language models, enabling faster and more efficient deployment of AI models in production environments. Their responsibilities often include integrating LLMs into applications, optimizing model performance, and ensuring scalability for real-time use cases. They may also collaborate with data scientists and engineers to manage resources and streamline AI workflows.

How does a vLLM engineer typically collaborate with data scientists and product teams during model deployment?

VLLM Engineers work closely with data scientists to understand the specific requirements and fine-tuning needs of large-scale language models. They are often responsible for integrating these models into production systems, ensuring scalability and efficiency. Collaboration with product teams is crucial to align model capabilities with user needs and to troubleshoot real-world application challenges. Frequent communication and agile workflows are common, as updates or optimizations may be needed rapidly based on feedback from both teams.

What are the key skills and qualifications needed to thrive as a machine learning engineer working with vLLM, and why are they important?

To thrive as a Machine Learning Engineer specializing in vLLM (a high-throughput LLM inference library), you need a strong understanding of machine learning principles, deep learning frameworks, and experience with Python programming. Familiarity with tools like PyTorch, CUDA, distributed computing, and cloud platforms, as well as relevant certifications in ML or data engineering, is highly valuable. Strong problem-solving, collaboration, and communication skills are essential for optimizing model performance and integrating with cross-functional teams. These capabilities ensure effective deployment and scaling of large language models, driving innovation and efficiency in AI applications.

What is the difference between Vllm vs Data Analyst?

AspectVllmData Analyst
Required CredentialsTypically requires knowledge of machine learning, AI, and programming languages like Python or RRequires skills in statistics, Excel, SQL, and data visualization tools
Work EnvironmentOften in tech companies, research labs, or AI-focused teamsCommonly in business, finance, healthcare, and marketing sectors
Industry UsageEmerging role in AI and machine learning projectsEstablished role in data-driven decision making
Common Search/ComparisonVllm vs Data Analyst

The main difference between Vllm and Data Analyst lies in their focus and skill set. Vllm professionals specialize in AI and machine learning models, often working in tech environments, while Data Analysts focus on interpreting data to inform business decisions. Both roles require analytical skills, but Vllm roles demand programming and AI expertise, whereas Data Analysts emphasize statistical analysis and data visualization.

What are popular job titles related to Vllm jobs in Tennessee?

For Vllm jobs in Tennessee, the most frequently searched job titles are:

What job categories do people searching Vllm jobs in Tennessee look for?

The top searched job categories for Vllm jobs in Tennessee are:

What cities in Tennessee are hiring for Vllm jobs?

Cities in Tennessee with the most Vllm job openings:

Vllm jobs near you
Infographic showing various Vllm job openings in Tennessee as of August 2026, with employment types broken down into 93% Full Time, 4% Part Time, and 3% Contract. Highlights an 77% Physical, 6% Hybrid, and 17% Remote job distribution.

Machine Learning Engineer at Gravity IT Resources Nashville, TN

Nashville, TN • On-site

Shell Lubricants Hub Hamburg
See more jobs at Shell Lubricants Hub Hamburgexternal

$110 - $150/hr

Other

Posted 24 days ago

Job description

Job Description

Machine Learning Engineer

Employment Type: Full-Time

Location: Nashville, TN (hybrid)

About the Role

We’re hiring a Maching Learning Engineer to design and deploy AI systems end-to-end — from data preparation and evaluation to model fine-tuning, inference, and agentic workflows. You’ll work closely with product and engineering teams to deliver reliable, cost-effective, and scalable LLM-powered solutions on AWS.

What You’ll Do
  • End-to-End GenAI Solutions: Scope problems, choose the right approach (prompt engineering, fine-tuning, agents), implement, evaluate, and deploy.
  • Data & SQL: Write efficient SQL for analytics and data prep; manage schemas and pipelines for model training and inference.
  • Model Training & Fine-Tuning: Run supervised fine-tuning (PEFT/LoRA/QLoRA), optimize prompts, and manage experiment tracking/evaluation.
  • Agentic Systems: Build agent workflows with tool use, memory, and safety/guardrails.
  • Inference & Deployment: Package services with Docker, optimize latency and cost (batching, caching, quantization), and deploy on AWS (ECS, EKS, SageMaker, Lambda with GPU acceleration).
  • MLOps & Observability: Set up CI/CD for models/prompts; maintain offline/online evaluation pipelines, monitoring, and rollback strategies.
  • Security & Compliance: Implement data governance, PHI/PII protections, and guardrails against prompt injection and unsafe outputs.
  • Cross-Functional Collaboration: Work with product managers and engineers to align GenAI capabilities with product goals; clearly document and communicate trade-offs.
  • Production Readiness: Lead conversations around scaling, monitoring, and maintaining GenAI systems in production environments.
Minimum Qualifications
  • 5+ years of Software/ML engineering experience, including 2+ years building and deploying GenAI/LLM systems.
  • MS/PhD in Computer Science, Data Science, or equivalent experience.
  • Strong SQL and Python skills with solid software engineering fundamentals.
  • Experience with agent frameworks (LangGraph, AutoGen, CrewAI) and tool-driven agents.
  • Hands‑on with deep learning (PyTorch or TensorFlow) and LLM fine‑tuning (SFT/PEFT like LoRA/QLoRA).
  • Production experience with Docker and AWS (ECS, EKS, SageMaker, Lambda, or GPU services).
  • Experience building scalable data and model pipelines for training and deployment.
  • Familiarity with prompt engineering, evaluation frameworks (LLM‑as‑judge, metrics), and offline test harnesses.
  • Understanding of security & compliance for sensitive data (e.g., PHI/PII).
  • Excellent problem‑solving, communication, and documentation skills.
Preferred Qualifications
  • Experience with inference optimization: quantization (bitsandbytes, GPTQ/AWQ), batching, caching, or vLLM.
  • Background in healthcare, including HIPAA compliance or medical data handling.
  • Experience with experiment tracking (MLflow, W&B), CI/CD for ML, and monitoring tools (Prometheus, Grafana).
  • Familiarity with major LLM APIs and open‑source models (OpenAI, Anthropic, Llama, Mistral).
Tech Stack
  • Languages: Python, SQL
  • DL/LLM: PyTorch, TensorFlow, Hugging Face, PEFT/TRL, vLLM
  • Data: Snowflake, Postgres
  • Cloud: AWS (ECS, EKS, SageMaker, Lambda)
  • MLOps: Docker, CI/CD, MLflow, or W&B
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