1

Intern Ai Coding Trainer Jobs in Tennessee (NOW HIRING)

... training as needed * Ensure compliance with public-sector procurement rules, IT governance ... Experience with code scanning tools (SonarQube, Snyk, Checkmarx, Veracode, Fortify, GitHub Advanced ...

Own a multi-level AI training curriculum spanning foundational to advanced skills across the firm ... Design, facilitate, and continuously refresh workshops, demos, intern programs, and community ...

Own a multi-level AI training curriculum spanning foundational to advanced skills across the firm ... Design, facilitate, and continuously refresh workshops, demos, intern programs, and community ...

We invest in skills training and provide opportunities for career development to help you grow ... to locate and code problems. Key Activities & Responsibilities • Identify processes and ...

New

We invest in skills training and provide opportunities for career development to help you grow ... to locate and code problems. Key Activities & Responsibilities • Identify processes and ...

Build or configure AI-powered applications using tools such as OpenAI, Copilot, or other low-code ... Build foundational AI literacy across departments through training and enablement sessions

Build or configure AI-powered applications using tools such as OpenAI, Copilot, or other low-code ... Build foundational AI literacy across departments through training and enablement sessions

Define and execute the technical roadmap for AI initiatives focused on code generation, agentic ... Guidearchitectural decisions for distributed training, inference, tool integration, and scalable ...

next page

Showing results 1-20

Intern Ai Coding Trainer information

How does an Intern AI Coding Trainer typically collaborate with senior engineers and learners during a project?

As an Intern AI Coding Trainer, you will often act as a bridge between senior engineers and learners by assisting with coding workshops, answering technical questions, and helping to create or refine training materials. You'll collaborate with senior engineers to understand project requirements and teaching strategies, while also providing learners with hands-on support and feedback. This role offers a dynamic environment where you can develop both your technical and communication skills, and gain valuable insight into AI project workflows.

What is the difference between Intern Ai Coding Trainer vs Intern Data Science Intern?

AspectIntern Ai Coding TrainerIntern Data Science Intern
Required CredentialsBasic programming knowledge, coursework in AI or codingBasic programming, statistics, or data analysis coursework
Work EnvironmentTech companies, educational platforms, AI startupsData-driven companies, research labs, tech firms
Employer & Industry UsageUsed in AI-focused roles, coding bootcamps, online educationCommon in data analysis, research projects, analytics teams

Intern Ai Coding Trainers typically focus on teaching and guiding students or junior developers in AI coding skills, often within educational or tech environments. Intern Data Science Interns work on data analysis, modeling, and research tasks. While both roles require programming knowledge, the AI Coding Trainer emphasizes teaching and training, whereas the Data Science Intern focuses on data-driven project work.

What are Intern AI Coding Trainers?

Intern AI Coding Trainers are individuals, typically students or recent graduates, who assist in teaching and developing artificial intelligence (AI) and programming skills, often as part of an internship program. They may help design coding exercises, provide feedback to learners, and support classroom or online instruction. Their role often involves learning on the job while contributing to educational projects and gaining experience in both AI and teaching methodologies.

What are the key skills and qualifications needed to thrive as an Intern AI Coding Trainer, and why are they important?

To thrive as an Intern AI Coding Trainer, you need a solid understanding of programming languages (such as Python), foundational knowledge in machine learning concepts, and preferably progress toward a degree in computer science or a related field. Familiarity with tools like TensorFlow, PyTorch, Jupyter Notebooks, and version control systems is typically required. Strong communication, patience, and the ability to explain complex technical concepts simply are crucial soft skills for this role. These skills ensure effective training delivery, foster learner engagement, and contribute to the successful development of AI skills in trainees.
What are the most commonly searched types of Ai Coding Trainer jobs in Tennessee? The most popular types of Ai Coding Trainer jobs in Tennessee are:

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Nashville, TN • Remote

$118K - $156K/yr

Full-time

Posted 16 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.