ZipRecruiter

Log In

1

Python Llm Jobs in Charleston, WV (NOW HIRING)

Bjak

LLM Application Engineer

Charleston, WV · Remote

Python * LLM APIs and model providers, including OpenAI-compatible APIs and open-weight models * Agent frameworks and orchestration systems * Vector databases and retrieval systems * Backend services ...

New

Filevine

Senior Backend Engineer - Python

Charleston, WV · Remote

$165K - $215K/yr

Mentor mid-level and junior engineers in async Python best practices, pydantic -ai agent design ... offs, and LLM behavior to non-technical stakeholders and cross-functional partners. * Growth ...

AHEAD

Full Stack Engineer

Charleston, WV · Remote

$160K - $190K/yr

We're hiring a full stack engineer who is comfortable across all parts of the stack (Python, JavaScript, React/Vue, APIs, AI/LLM integration) and can help ship production-grade UI using established ...

Bespoke Labs

Machine Learning Engineer

Charleston, WV · On-site

... Python and deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed precision) Hands-on experience with LLM post-training -- SFT, RLHF, PPO, DPO, or reward model ...

3Pillar

AI Data Architect

Charleston, WV · Remote

$65.25 - $84/hr

Python, SQL, Snowflake/Databricks, AWS (S3, Glue, EKS, Bedrock, Kinesis, Redshift), Docker, Kubernetes, Terraform, GitHub Actions, LangChain, LlamaIndex, LLM APIs (OpenAI, AWS Bedrock, Claude ...

Bjak

Machine Learning Platform Engineer

Charleston, WV · Remote

Python * PyTorch / JAX * LLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM * Cloud infrastructure * Distributed systems * ML/data pipelines and workflow orchestration * GPU ...

New

Lyra Health

Senior AI/ML Platform Engineer

Charleston, WV · Remote

$143K - $197K/yr

Experience with modern async Python web frameworks (e.g., FastAPI) and handling data streaming ... Familiarity with LLM orchestration frameworks (e.g., LangChain, LlamaIndex) and integrating with ...

SQUIRE

Director, Data Engineering

Charleston, WV · Remote

$200K - $230K/yr

Advanced SQL and Python skills, along with strong MDM fundamentals, with the technical depth to ... Direct experience setting AI and LLM enablement strategy and building with LLM tooling

SailPoint

Staff Machine Learning Engineer

Charleston, WV · On-site

Design, implement, and optimize ML models (supervised, unsupervised, and LLM-based) that power both ... Strong programming skills in Python and proficiency with ML frameworks such as PyTorch, TensorFlow ...

next page

Showing results 1-20

All JobsPython Llm JobsPython Llm Jobs in Charleston, WV

Python Llm information

See Charleston, WV salary details

$12

$56

$83

How much do python llm jobs pay per hour?

As of Aug 13, 2026, the average hourly pay for python llm in Charleston, WV is $56.97, according to ZipRecruiter salary data. Most workers in this role earn between $46.97 and $64.71 per hour, depending on experience, location, and employer.

What is a Python LLM?

A Python LLM job involves working with Large Language Models (LLMs) using Python to develop, fine-tune, and deploy AI models. Responsibilities may include data preprocessing, prompt engineering, model optimization, and integration with applications. Professionals in this role often work with frameworks like TensorFlow, PyTorch, or Hugging Face Transformers. They may also contribute to improving model efficiency, reducing bias, and ensuring ethical AI usage.

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

To excel as a Python LLM (Large Language Model) Engineer, you need strong skills in Python programming, machine learning, and natural language processing, typically supported by a degree in computer science or a related field. Proficiency with libraries such as TensorFlow, PyTorch, Hugging Face Transformers, and experience with model deployment platforms are often essential, alongside certifications in AI or data science. Effective communication, problem-solving abilities, and collaboration are important soft skills for working in interdisciplinary teams and delivering results in dynamic environments. These skills ensure the development, fine-tuning, and deployment of advanced language models that meet both technical and business objectives.

What are some common challenges faced by Python LLM engineers in their daily work?

Python LLM Engineers often encounter challenges related to optimizing model performance, managing large datasets, and adapting models to specific business needs. Working with large-scale language models requires balancing computational resource limitations with the need for high accuracy and efficiency. Collaboration with data scientists, product managers, and DevOps engineers is routine to ensure seamless model integration and deployment. Staying updated on the latest advancements in NLP and continuously improving models based on user feedback are also important aspects of the role.

Python Llm jobs near you
Infographic showing various Python Llm job openings in Charleston, WV as of August 2026, with employment types broken down into 2% Internship, 84% Full Time, 7% Part Time, and 7% Contract. Highlights an 79% Physical, 5% Hybrid, and 16% Remote job distribution, with an average salary of $118,503 per year, or $57 per hour.

LLM Application Engineer

Bjak

Charleston, WV • Remote

Full-time

Posted 2 days ago

New

Job description

About A1

There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting.

Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over ~90%* reduced time.

About the Role

As an LLM Application Engineer, you will build the intelligence layer that powers A1's AI experiences.

You will work at the intersection of LLMs, software engineering, and product - designing agent workflows, improving model behaviour, and turning AI capabilities into reliable user experiences.

You will own problems end-to-end, from understanding user needs, designing Agentic workflows, integrating models and tools, building evaluation system and continuously improving AI behaviour in production.

Focus

  • Build and ship LLM-powered applications and AI agent workflows

  • Design systems for reasoning, planning, memory, tool uuse and multi-step execution

  • Build reliable orchestration pipelines that turn probabilistic model outputs into predictable, observable, and safe actions

  • Integrate LLMs with APIs, databases, search, internal services, and external tools.

  • Develop prompting, context engineering, structured outputs, tool-calling, and other techniques to improve model behaviour

  • Build evaluation frameworks and datasets to measure AI quality, reliability, and regressions

  • Debug AI systems across the entire stack—from model behaviour and prompts to orchestration, backend services, and product UX

  • Optimise AI systems for quality, latency, and cost

  • Work closely with product and engineering teams to turn ambiguous product problems into working AI solutions

  • Establish production practices for observability, tracing, experimentation, evaluation, and continuous improvement

Tech Stack

  • Python

  • LLM APIs and model providers, including OpenAI-compatible APIs and open-weight models

  • Agent frameworks and orchestration systems

  • Vector databases and retrieval systems

  • Backend services, APIs, and distributed systems

  • PyTorch / JAX

Ideal Experience

  • Strong software engineering fundamentals with experience building AI-powered applications

  • Hands-on experience with LLMs, generative AI, or agent-based systems

  • Experience designing prompts, workflows, evaluations, or AI behaviour

  • Ability to write clean, production-quality code

  • Comfortable working across abstraction layers (model → system → product)

  • Strong problem-solving skills in ambiguous, fast-moving environments

  • Bias toward shipping, iteration, and continuous improvement

Outcomes

  • AI features reach production quickly and deliver measurable user impact

  • LLM-powered workflows are reliable, scalable, observable, and maintainable

  • AI quality improves through systematic evaluation, experimentation, and iteration

  • AI workflows become increasingly predictable, efficient, and cost-effective

  • Complex AI capabilities are translated into simple, intuitive user experiences