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Mistral Jobs in Tennessee (NOW HIRING)

Pwc

AI Engineer

Nashville, TN

$50K - $112K/yr

... Mistral, and Gemma, for local and cloud deployment using quantization, inference acceleration, and model-routing techniques - Designing agent harnesses and implementing context engineering, memory ...

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Mistral information

See Tennessee salary details

$13.6K

$219K

$351.2K

How much do mistral jobs pay per year?

As of Aug 23, 2026, the average yearly pay for mistral in Tennessee is $219,004.00, according to ZipRecruiter salary data. Most workers in this role earn between $181,500.00 and $272,300.00 per year, depending on experience, location, and employer.

What is a Mistral?

Mistral jobs refer to roles related to Mistral, which can indicate either a workflow orchestration service in IT or positions at Mistral AI, a company specializing in artificial intelligence and large language models. In the context of workflow orchestration, Mistral jobs involve creating, managing, and monitoring automated workflows, often in cloud or DevOps environments. At Mistral AI, jobs can include research, software engineering, and AI model development. Responsibilities usually focus on building scalable, efficient systems or advancing state-of-the-art machine learning technologies.

What are the key skills and qualifications needed to thrive as a Mistral engineer?

To thrive as a Mistral Engineer, you need a solid background in software engineering, machine learning, and natural language processing, often supported by a degree in computer science or a related field. Familiarity with frameworks like PyTorch or TensorFlow, experience with distributed systems, and version control tools such as Git are typically required. Strong problem-solving skills, collaboration, and adaptability help individuals excel in this dynamic, innovative environment. These competencies are crucial for driving advancements in AI technology and delivering robust, scalable solutions.

What are some typical challenges faced by Mistral engineers when integrating AI models into production environments?

Mistral engineers often encounter challenges such as ensuring model scalability, managing latency, and maintaining robust security when deploying AI models into production. They must frequently collaborate with data scientists, DevOps, and product teams to fine-tune models, monitor real-world performance, and address unexpected behavior. Staying updated with rapid advancements in machine learning frameworks and cloud infrastructure is also crucial. Effective communication and agile problem-solving are key to overcoming these hurdles and delivering reliable AI solutions.

What is the difference between Mistral vs Data Scientist?

AspectMistralData Scientist
Required CredentialsTypically requires a background in engineering, physics, or related fields; certifications are optionalRequires a degree in computer science, statistics, or related fields; certifications like Certified Data Scientist are common
Work EnvironmentOften works in research labs, tech companies, or startups focusing on AI and machine learningWorks in various industries including finance, healthcare, and tech, analyzing data to inform decisions
Employer & Industry UsageUsed mainly in AI research and development, especially in natural language processingWidely used across industries for data analysis, predictive modeling, and business insights

While both Mistral and Data Scientists work with advanced technology, Mistral typically focuses on AI research and development, often requiring a strong engineering background. Data Scientists analyze data to generate insights across industries. The roles overlap in technical skills but differ in focus and application.

What is it like to work at Mistral?

Working as a Mistral involves engaging in roles that may require technical skills, teamwork, and adherence to safety protocols. Employees often work in collaborative environments with a focus on efficiency and quality, and may need to be adaptable to changing project demands or schedules.

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

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

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

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

What cities in Tennessee are hiring for Mistral jobs?

Cities in Tennessee with the most Mistral job openings:

Mistral jobs near you
Infographic showing various Mistral job openings in Tennessee as of August 2026, with employment types broken down into 95% Full Time, 1% Part Time, and 4% Contract. Highlights an 81% Physical, 2% Hybrid, and 17% Remote job distribution, with an average salary of $219,004 per year, or $105.3 per hour.

Machine Learning Engineer at Gravity IT Resources Nashville, TN

Shell Lubricants Hub Hamburg

Nashville, TN • On-site

$110 - $150/hr

Other

Posted 18 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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