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Generative Ai Testing Jobs in Tennessee (NOW HIRING)

BravoTech

AI Architect

Nashville, TN · On-site

AI Architect - Hybrid Onsite (2 days/ week) in Nashville TN Seeking a visionary AI Architect to ... testing, incident triage, knowledge discovery, and business process automation). 3. Generative ...

CGI Inc.

AI/ML Engineer

Knoxville, TN · On-site

This role blends full stack engineering with modern AI/ML, integrating generative AI and advanced ... CI/CD with Jenkins/GitLab/Bitbucket; testing frameworks; package managers. o Cloud: Production ...

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Generative Ai Testing information

What is the difference between Generative Ai Testing vs Data Scientist?

AspectGenerative Ai TestingData Scientist
Required CredentialsKnowledge of AI models, testing tools, programming skillsStatistics, programming, data analysis certifications
Work EnvironmentAI development teams, testing labs, tech companiesResearch labs, tech firms, finance, healthcare
Employer & Industry UsageAI product testing, quality assurance in techData analysis, predictive modeling across industries

Generative Ai Testing focuses on evaluating and validating AI-generated content and models, ensuring quality and accuracy. Data Scientists analyze data, build models, and derive insights. While both roles require programming and AI knowledge, Generative Ai Testing emphasizes testing processes, whereas Data Scientists focus on data analysis and model development.

How much do AI testers get paid?

AI testers, involved in evaluating and validating generative AI models, typically earn salaries ranging from $60,000 to $120,000 annually depending on experience, location, and company size. Entry-level positions may start lower, while experienced testers with specialized skills in machine learning and data analysis can earn higher wages.

Is AI testing a good career?

AI testing, including roles like Generative AI Testing, is a growing field with increasing demand for skills in machine learning, data analysis, and programming. It offers opportunities in tech companies, research labs, and startups, often requiring knowledge of AI frameworks and testing tools. The career can be stable and rewarding for those with technical expertise and an interest in AI development and quality assurance.

What are the key skills and qualifications needed to thrive as a Generative AI Testing Specialist, and why are they important?

To thrive as a Generative AI Testing Specialist, you need a robust understanding of machine learning principles, model evaluation techniques, and a background in computer science or a related field. Familiarity with tools such as Python, TensorFlow, PyTorch, and model evaluation frameworks, as well as experience with automated testing platforms, is typically required. Analytical thinking, attention to detail, and strong communication skills help you identify model weaknesses and collaborate effectively with development teams. These skills are crucial to ensure the reliability, safety, and ethical deployment of generative AI solutions.

What are some common challenges faced when testing generative AI models, and how can I prepare to address them in this role?

Testing generative AI models often involves unique challenges such as evaluating the quality and relevance of generated content, detecting bias or inappropriate outputs, and ensuring model consistency across various prompts. You may work closely with data scientists and engineers to create robust evaluation frameworks and develop automated as well as manual testing strategies. Familiarity with prompt engineering, statistical evaluation techniques, and domain-specific knowledge will help you address these challenges effectively. Proactively staying updated on industry best practices and collaborating with cross-functional teams are key to success in this dynamic field.

What is a $900,000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers, AI research directors, or executive positions, often requiring advanced skills, extensive experience, and sometimes specialized certifications. These roles usually involve leading AI development projects, strategic planning, and overseeing AI teams in large organizations or tech companies.

How do I become an AI tester?

To become an AI tester, you should have a strong understanding of machine learning concepts, programming skills in languages like Python, and experience with data annotation and model evaluation. Familiarity with AI development tools and testing frameworks, along with attention to detail, is essential for identifying issues in AI systems.

What is Generative AI Testing?

Generative AI Testing refers to the process of evaluating and validating AI systems, particularly those that generate content such as text, images, or code. This type of testing focuses on assessing the accuracy, reliability, fairness, and safety of generative models to ensure they function as intended and avoid producing harmful or biased outputs. Testers use various methods, including automated and manual techniques, to check for issues like hallucinations, inappropriate content, or security vulnerabilities. The goal is to build trust in generative AI systems and ensure they meet quality and ethical standards before deployment.
What are popular job titles related to Generative Ai Testing jobs in Tennessee? For Generative Ai Testing jobs in Tennessee, the most frequently searched job titles are:
What job categories do people searching Generative Ai Testing jobs in Tennessee look for? The top searched job categories for Generative Ai Testing jobs in Tennessee are:
What cities in Tennessee are hiring for Generative Ai Testing jobs? Cities in Tennessee with the most Generative Ai Testing job openings:
Generative Ai Testing jobs near you
Infographic showing various Generative Ai Testing job openings in Tennessee as of June 2026, with employment types broken down into 98% Full Time, and 2% Contract. Highlights an 65% Physical, 4% Hybrid, and 31% Remote job distribution.
AI Architect

AI Architect

BravoTech

Nashville, TN • On-site

Contractor

Posted 19 days ago

Job description


AI Architect
- Hybrid Onsite (2 days/ week) in Nashville TN
Seeking a visionary AI Architect to lead the design, governance, and implementation of next-generation Generative AI and Agentic Systems across the enterprise. This role is responsible for translating complex business problems into scalable, secure, and production-grade AI solutions, with a strong emphasis on autonomous agents, intelligent workflows, and AI-augmented SDLC ecosystems.
The ideal candidate brings a rare combination of enterprise-scale system architecture expertise, deep Generative AI knowledge, and hands-on engineering leadership, enabling them to operate seamlessly across strategy, design, and execution phases.
Years of Experience: 12+ Years
Key Responsibilities
1. Architecture & System Design
  • Own the end-to-end architecture of large-scale, distributed GenAI platforms, including microservices, data pipelines, and AI inference layers.
  • Define reference architectures and design patterns for Generative AI, agentic workflows, and AI-enabled enterprise platforms.
  • Ensure all systems are secure, scalable, fault-tolerant, cost-efficient, and production-ready.
2. Agentic Systems & Workflow Orchestration
  • Design and implement autonomous and semi-autonomous multi-agent systems using frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or custom orchestration engines.
  • Enable agent collaboration, task planning, memory management, tool use, and self-reflection capabilities.
  • Architect agent-driven enterprise workflows (e.g., code generation, testing, incident triage, knowledge discovery, and business process automation).
3. Generative Model Engineering
  • Lead model selection, fine-tuning, and optimization of Large Language Models (LLMs) and Small Language Models (SLMs), including OpenAI, Anthropic, Gemini, LLaMA, Mistral, and domain-specific models.
  • Apply Parameter-Efficient Fine-Tuning (PEFT) techniques such as LoRA, QLoRA, adapters, and distillation to optimize cost and performance.
  • Oversee Retrieval-Augmented Generation (RAG) architectures, vector search, prompt engineering, memory augmentation, and evaluation pipelines.
  • Drive experimentation with Diffusion models, GANs, and multimodal models where applicable.
4. LLMOps / MLOps & Cloud Infrastructure
  • Architect and standardize LLMOps/MLOps pipelines for training, evaluation, deployment, observability, and lifecycle management.
  • Design cloud-native AI platforms on AWS, Azure, or GCP, leveraging GPU/TPU infrastructure, Kubernetes, and serverless computing patterns.
  • Implement comprehensive monitoring for latency, hallucinations, model drift, cost usage, security events, and SLA compliance.
  • Optimize inference using techniques such as quantization, batching, caching, and intelligent model routing.
5. AI-Driven SDLC & Developer Experience
  • Architect AI-augmented Software Development Lifecycle (SDLC) systems, including:
    • Agentic code generation and refactoring
    • Automated test generation and validation
    • Intelligent CI/CD workflows
    • AI-powered documentation and knowledge management
  • Partner with platform and Developer Experience (DevEx) teams to embed AI into developer tooling and workflows.
6. Governance, Security & Responsible AI
  • Define AI governance frameworks covering model risk, data privacy, lineage, explainability, bias detection, and regulatory compliance.
  • Ensure alignment with security, legal, and regulatory requirements (e.g., HIPAA, SOC2, GDPR, as applicable).
  • Establish robust guardrails for safe agent behavior, access control, prompt injection defense, and data leakage prevention.
7. Strategy, Leadership & Collaboration
  • Serve as a technical thought leader and advisor to executive stakeholders.
  • Lead and mentor senior engineers, data scientists, and AI researchers.
  • Manage multiple concurrent initiatives while balancing innovation with reliable delivery.
  • Drive buy-vs-build decisions, vendor evaluations, and strategic roadmap planning.
  • Evangelize AI best practices across engineering, product, and data teams.
Required Qualifications
Core Engineering & Architecture
  • 12+ years of experience in enterprise-grade full-stack or platform architecture.
  • Strong background in product engineering, distributed systems, and microservices.
  • Demonstrated ability to design mission-critical, high-availability systems.
AI / ML & Generative AI Expertise
  • Strong theoretical and hands-on expertise in:
    • Deep Learning (CNN, RNN, LSTM)
    • Transformer architectures and attention mechanisms
  • Deep experience with Generative AI, including:
    • Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and prompt engineering
    • GANs and Diffusion models
  • Proven experience integrating with OpenAI, Azure OpenAI, Hugging Face, or equivalent platforms.
Technical Stack
  • Expert-level proficiency in Python; strong working knowledge of C++ and Java.
  • Extensive experience with PyTorch, TensorFlow, and Keras.
  • Expertise in designing RESTful APIs, GraphQL, and event-driven architectures using Kafka or RabbitMQ.
  • Strong understanding of databases, vector stores, and streaming systems.
Cloud & DevOps
  • Proven track record of deploying and operating large-scale ML/AI workloads in production.
  • Hands-on experience with Kubernetes, Docker, and Infrastructure as Code (IaC) tools (Terraform, Bicep, or CloudFormation).
  • Familiarity with CI/CD pipelines, observability stacks, and secure cloud networking.
Preferred Other Skills
  • Experience in Healthcare, Payer, or Life Sciences domains, including regulated data environments.
  • Exposure to edge AI, on-device inference, or real-time decision-making systems.
  • Contributions to open-source AI/ML projects or published technical thought leadership.
  • Experience building internal AI platforms or AI Centers of Excellence (CoE).
What Success Looks Like
  • Enterprise-scale Generative AI platforms run reliably and efficiently in production.
  • Autonomous agents delivering measurable productivity gains across the organization.
  • Secure, governable, and cost-efficient AI ecosystems.
  • Engineering teams are empowered by AI-native tooling and workflows.
  • Clear architectural vision consistently aligns with strategic business outcomes.

Meet Your Recruiter
Perry Gross
Text me about this job - 972-419-1628
Please include your name and Job Title in your Text.
Thanks!
  • 972-419-1628
  • pgross@bravotech.com

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