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Full Time Llm Engineer Jobs (NOW HIRING)

Sr. AI/ML Engineer (LLM)

Miami, FL · On-site

$99K - $137K/yr

Role Description This is a full-time, on-site role located in Miami, FL, for a Senior AI/ML ... Collaborate with product managers, data scientist, and software engineers to integrate LLM-based ...

Showing results 21-40

Full Time Llm Engineer information

What are the key skills and qualifications needed to thrive as a Full Time LLM Engineer, and why are they important?

To thrive as a Full Time LLM Engineer, you need a strong background in computer science, machine learning, and natural language processing, often supported by a relevant degree and experience with large language models. Familiarity with frameworks such as PyTorch or TensorFlow, version control systems like Git, and experience deploying models using cloud platforms or MLOps tools are typically required. Strong problem-solving, collaboration, and communication skills help you work effectively within cross-functional teams and adapt to evolving technologies. These skills ensure the design, optimization, and deployment of robust language models that meet real-world application needs.

What are some common challenges faced by Full Time LLM Engineers when deploying large language models in production environments?

Full Time LLM Engineers often encounter challenges related to optimizing model performance, managing infrastructure costs, and ensuring reliable scaling in production. Handling inference speed and latency is critical, especially when integrating with real-time applications. Additionally, monitoring model behavior for biases, drift, and security vulnerabilities requires ongoing collaboration with data scientists and operations teams. Staying updated on the latest advancements and tools in the LLM landscape is essential for maintaining effective and efficient deployments.

What are Full Time LLM Engineers?

Full Time LLM Engineers are professionals who specialize in developing, fine-tuning, and deploying large language models (LLMs) like GPT, BERT, or similar AI models. They work with machine learning frameworks, manage data pipelines, and optimize model performance for various applications such as chatbots, content generation, and natural language processing tasks. These engineers often collaborate with data scientists, product teams, and software engineers to integrate LLMs into products and services. Their responsibilities may also include monitoring model outputs, ensuring ethical AI use, and staying updated with advancements in the field.

What is the difference between Full Time Llm Engineer vs Machine Learning Engineer?

AspectFull Time Llm EngineerMachine Learning Engineer
Required CredentialsAdvanced degree in law, computer science, or related fields; knowledge of legal data and NLPDegree in computer science, data science, or related fields; expertise in algorithms and data modeling
Work EnvironmentLegal tech companies, AI firms focusing on legal applications, research institutionsTech companies, startups, research labs working on AI and data-driven solutions
Employer & Industry UsageLegal industry, AI legal tools, compliance firmsTechnology industry, AI product development, data-driven applications

While both roles involve AI and data, a Full Time Llm Engineer specializes in legal language models and legal data, whereas a Machine Learning Engineer works broadly across various AI applications and industries. The Llm Engineer focuses on legal-specific NLP tasks, requiring legal knowledge combined with AI skills, while the Machine Learning Engineer has a broader scope in AI development across sectors.

More about Full Time Llm Engineer jobs
What cities are hiring for Full Time Llm Engineer jobs? Cities with the most Full Time Llm Engineer job openings:
What are the most commonly searched types of Llm Engineer jobs? The most popular types of Llm Engineer jobs are:
What states have the most Full Time Llm Engineer jobs? States with the most job openings for Full Time Llm Engineer jobs include:
Infographic showing various Full Time Llm Engineer job openings in the United States as of July 2026, with employment types broken down into 96% Full Time, 1% Part Time, and 3% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution.

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Austin, TX • Remote

$121K - $160K/yr

Full-time

Posted 10 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.