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Llm Engineer Jobs in Tennessee (NOW HIRING)

LLM development and fine-tuning strategies, best practices, and standards to enhance AI ML model ... engineers for the deployment of machine learning models into production environments, ensuring ...

LLM development and fine-tuning strategies, best practices, and standards to enhance AI ML model ... engineers for the deployment of machine learning models into production environments, ensuring ...

Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector/hybrid search, and retrieval/evaluation telemetry. * Deliver governed datasets and feature ...

AI Lead Engineer

Nashville, TN · On-site

$99K - $130K/yr

The AI Lead Engineer will define the technical vision and architecture for AI capabilities, lead ... • LLM model selection, configuration, and prompt tooling • Model fine‑tuning and ...

AI Lead Engineer

Nashville, TN · On-site

$99K - $130K/yr

Drive LLM inference optimization initiatives, including: * Prompt engineering and prompt tuning * Response and retrieval caching strategies * Latency reduction and throughput optimization * Cost ...

AI Lead Engineer

Nashville, TN · On-site

$99K - $130K/yr

LLM model selection, configuration, and prompt tooling * Model finetuning and customization ... Prompt engineering and prompt tuning * Response and retrieval caching strategies * Latency ...

AI Lead Engineer

Nashville, TN · On-site

$99K - $130K/yr

LLM model selection, configuration, and prompt tooling * Model finetuning and customization ... Prompt engineering and prompt tuning * Response and retrieval caching strategies * Latency ...

... LLM-powered products in production. · Hands-on experience with AI/LLM systems (agents, RAG, automation, evaluation). · Strong software engineering skills (Python required; modern web/TypeScript a ...

LLM Concepts * RAG Architecture * MS OpenAI * GPT40 41 * O3 Mini Mandatory Skills : Prompt Engineering & RAG,Retrieval Augmented Generation,Fine Tuning Large Language Models,Prompt Engineering

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Llm Engineer information

See Tennessee salary details

$23

$48

$69

How much do llm engineer jobs pay per hour?

As of Aug 11, 2026, the average hourly pay for llm engineer in Tennessee is $48.68, according to ZipRecruiter salary data. Most workers in this role earn between $39.28 and $56.49 per hour, depending on experience, location, and employer.

What does an LLM engineer do?

An LLM Engineer designs, develops, and optimizes applications that leverage large language models (LLMs). They fine-tune models, integrate them into products, and improve performance through prompt engineering and model customization. This role requires expertise in machine learning, natural language processing (NLP), and software development. LLM Engineers work closely with data scientists and developers to create AI-driven solutions for various applications such as chatbots, content generation, and code assistance.

How much do Llm engineers make?

Llm engineers typically earn between $100,000 and $180,000 annually, depending on experience, location, and company size. Senior roles or those with specialized skills in machine learning and natural language processing can command higher salaries, often exceeding $200,000 with bonuses and stock options.

What are the key skills and qualifications needed to thrive as an LLM engineer?

To thrive as an LLM Engineer, you need strong expertise in machine learning, natural language processing, and proficiency with Python, along with a solid understanding of transformer-based models and deep learning frameworks like PyTorch or TensorFlow. Familiarity with cloud platforms, version control systems (e.g., Git), and tools such as Hugging Face Transformers is typically required, and certifications in AI or data science can be advantageous. Excellent problem-solving, collaboration, and communication skills help you work effectively with interdisciplinary teams and present complex findings clearly. These skills enable you to develop, fine-tune, and deploy large language models efficiently in real-world applications.

What are the most commonly searched types of Llm Engineer jobs in Tennessee? The most popular types of Llm Engineer jobs in Tennessee are:
What are popular job titles related to Llm Engineer jobs in Tennessee? For Llm Engineer jobs in Tennessee, the most frequently searched job titles are:
What job categories do people searching Llm Engineer jobs in Tennessee look for? The top searched job categories for Llm Engineer jobs in Tennessee are:
What cities in Tennessee are hiring for Llm Engineer jobs? Cities in Tennessee with the most Llm Engineer job openings:
Infographic showing various Llm Engineer job openings in Tennessee as of August 2026, with employment types broken down into 89% Full Time, 7% Part Time, and 4% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $101,247 per year, or $48.7 per hour.

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Nashville, TN • Remote

$118K - $156K/yr

Full-time

Posted 28 days ago


Job description

Senior Software Engineer: Applied AI (Voice Agents & ML Systems)

AMC Health · Remote (US) · Full-time

The pitch

We build and operate production AI voice agents that hold real phone conversations in a regulated healthcare setting, plus the machine learning and LLM pipelines around them. This is one seat that spans four disciplines that rarely come together: real-time systems, LLM engineering, traditional machine learning, and serious cloud infrastructure, all in production, all with real consequences. If you are the kind of engineer who gets restless doing one thing, this role is the opposite problem.

What you'll work across

Real-time voice AI

  • Streaming, low-latency speech-to-speech systems built on modern LLMs
  • Telephony and real-time media (call control, live audio streaming)
  • Audio handling and the quirks of real human conversation (interruptions, timing, noise)
  • Concurrency on a latency-sensitive path, where p99 matters and a stall is something a caller hears

LLM engineering

  • Wrapping nondeterministic models in deterministic control so they behave reliably in production
  • Multi-model pipelines, prompt design, and cost/latency budgeting
  • Evaluation harnesses, including LLM-as-judge and automated agent-tests-agent approaches
  • Agentic tooling that gives AI systems safe, structured access to infrastructure

Traditional (non-LLM) machine learning

  • End-to-end ML pipelines: feature engineering, model training, and scheduled inference
  • Imbalanced, messy real-world data; calibration and explainability for non-technical consumers
  • Turning research notebooks into reproducible, auditable production pipelines

Cloud and infrastructure

  • Infrastructure as code across multiple environments (we run on AWS)
  • Managed compute, data, streaming, and orchestration services
  • Security engineering in a regulated setting: encryption, least-privilege access, strict data-handling discipline
  • Observability and telemetry-driven debugging, tracing a production issue from a metric anomaly to root cause

Plus occasional full-stack work on internal tools, and an engineering workflow that leans heavily on AI coding assistants, with human accountability for every change.

What you'll actually do

  • Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
  • Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
  • Build and operate LLM evaluation and batch-analysis pipelines
  • Own traditional ML workflows from data to scheduled production inference
  • Trace production issues from a metric anomaly to root cause, including building the evidence when the cause is a vendor

Must-haves

  • 7+ years building and operating production backend systems, with strong general-purpose programming skills (we work primarily in Python)
  • Experience running distributed systems in the cloud; comfortable debugging from telemetry to root cause
  • Hands-on production experience with LLMs or generative AI (any provider or framework), plus the judgment to know when not to use a model
  • Working fluency across the traditional machine learning lifecycle (you productionize; you do not need to publish)
  • Disciplined in a regulated environment: small, reviewable changes and careful handling of sensitive data

Nice-to-haves

  • Real-time media or telephony experience
  • Front-end / full-stack ability
  • ML pipeline experience, vector search, or embeddings
  • Fluency with AI coding assistants (our workflows assume them, with human accountability for every change)

How we work

Smallest correct change wins. Every behavior change is validated against the live system. Evidence over opinion in debugging. Code review is rigorous. Safety and privacy gate everything.

Work authorization (no exceptions)

This role is open only to US citizens and lawful permanent residents (Green Card holders). We cannot consider candidates who require visa sponsorship now or in the future, and we are unable to make exceptions of any kind.

How to apply

Please submit both of the following:

  • Your LinkedIn profile URL
  • A phone number where we can reach you

A resume is welcome but optional; the two items above are required.