AI Engineer

• Design, build, test, and deploy AI legal workflows and skills across firm-approved AI tools, supporting use cases such as contract review, due diligence, drafting, research, and summarization
• Partner directly with attorneys and knowledge management professionals to identify high-value use cases, map workflows, and translate practice needs into structured AI solutions
• Develop and maintain retrieval pipelines, document templates, and reusable workflow components with clear, attorney-ready documentation
• Build and execute evaluation frameworks to measure workflow quality, accuracy, and reliability; monitor performance and iterate based on feedback and outcomes
• Serve as a go-to resource for enablement and change management for developed workflows, partnering with the AI Adoption team to drive usage, deliver training sessions and workshops, 1:1 coaching, and develop practical playbooks and materials that support sustained adoption
• Develop and scale best-practice standards, use-case libraries, and playbooks that empower attorneys to responsibly and independently leverage AI tools while maintaining quality and risk controls
• Embed human-review checkpoints and audit mechanisms aligned with Firm governance, confidentiality, and professional responsibility requirements
• Collaborate closely with cross-functional stakeholders including the AI Product team, AI Adoption team, Knowledge & Innovation team, IT, Innovation Practice Group, and Professional Development to ensure aligned delivery and support
• Track adoption, usage, and impact metrics for deployed workflows and recommend refinements or expansion opportunities
• Manage ongoing maintenance of production workflows, including updates to prompts, retrieval sources, and guardrails in response to model and platform changes, user feedback, or evolving practice needs
• Represent the AI Products & Adoption team in internal meetings, committees, and practice group sessions

AI Legal Engineer

• The Applied Artificial Intelligence Engineer is part of the Education Design and Informatics team within the Office of Health Sciences Education at Vanderbilt University School of Medicine.
• The Education Design & Informatics Team (EDI) is responsible for technology, instructional design, data infrastructure, and quality improvement for Vanderbilt's MD program and affiliated health professions education.
• We are seeking an AI Engineer to help realize our vision for precision medical education.
• You will build intelligent features into our products, including LLM-powered tools, semantic search, recommendation systems, and AI integration.
• You will contribute to predictive modeling efforts, including early identification of at-risk learners.
• This is a high-ownership, foundational role where you will shape AI strategy and implementation.
• You will work closely with educational leadership to identify AI opportunities for improving medical training.

Applied Artificial Intelligence Engineer

• Build our agentic SDLC pipeline. Design, implement, and operate agentic workflows across the development lifecycle - planning, code generation, review, testing, documentation, and release that is all integrated with our GitOps and CI/CD infrastructure.
• Level up developer experience. Bring modern AI integrations into the daily workflow of our engineering team: coding agents, automated review, intelligent test generation, and self-service tooling that removes friction and multiplies output.
• Engineer grounded, trustworthy systems. Build retrieval-grounded and context-aware AI systems that produce reliable, verifiable results and design the evaluation harnesses that prove it.
• Establish governance and guardrails. Define and implement the policies, human-in-the-loop checkpoints, audit trails, and security controls that let us adopt agentic AI responsibly in environments where trust is non-negotiable.
• Formalize and scale. Turn what works into repeatable frameworks, playbooks, and internal platforms that extend agentic acceleration beyond engineering to the whole organization.
• Champion the practice. Evangelize, teach, and support AI adoption across the team; you'll be a force multiplier, not a silo.

AI Workflow Engineer

• Ship and debug code on a live, real-time voice pipeline
• Design control systems around LLMs
• 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

Senior Software Engineer – Applied AI

• The Senior Agentic AI Engineer is a hands-on builder responsible for designing, developing, and deploying AI-powered autonomous workflows and agentic systems.
• Design and build LLM-powered agentic workflows using Azure AI Foundry and Anthropic Claude APIs.
• Develop multi-step AI agent pipelines using orchestration frameworks integrated with enterprise systems.
• Implement Model Context Protocol (MCP) integrations to enable AI agents.
• Apply security-by-design principles throughout all AI agent development.
• Build, test, and iterate on prompts, system instructions, and agent reasoning chains.
• Partner with the Backend Integration Engineer to connect agent workflows to existing enterprise API infrastructure.
• Collaborate with the AI Business Analyst to translate business use cases into technical agent requirements.
• Participate in code reviews and maintain internal documentation.

Senior Engineer, AI Agentic – Transformation

• Own stakeholder relationships across Medbridge’s executive team, working with them to find the problems and AI enabled solutions that can expedite, or remove, the current blockers in the business.
• Design, build, and ship AI-powered features across our internal teams, from the user interface to the backend services and AI workflows behind it.
• Build full-stack applications using AI-native engineering workflows and tools such as Claude, Cursor, Codex, and OpenAI and Anthropic APIs.
• Develop APIs, backend services, and automation pipelines that support intelligent product capabilities and production-ready deployments.
• Apply software engineering, DevOps, and MLOps best practices to the testing, deployment, monitoring, evaluation, and optimization of AI solutions.
• Partner closely with our Product and Engineering teams to turn ambiguous goals into scalable internal products.
• Use AI tooling throughout your workflow (coding, code review, testing, and planning) and help push the team’s adoption forward.
• Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks, and bring what works back to the team.
• Instrument what you ship and use usage data and evidence to decide what to build, fix, or kill next.

Product Engineer – AI

• Leading the development of machine learning programs to identify patterns and automate decision-making processes
• Setting technical objectives for machine learning assignments and overseeing their execution
• Designing machine learning prototypes in collaboration with product teams and stakeholders
• Conducting research and studies to inform product development and application of machine learning techniques
• Implementing end-to-end machine learning solutions
• Authoring technical documentation, including white papers and technical manuals
• Evaluating machine learning solutions from internal and external partners
• Collaborating with external teams and representing the work group in resolving technical challenges related to machine learning projects

Senior Machine Learning Engineer, AI Recommendation – Search

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

• Define and evolve enterprise AI architecture, standards, reference models and implementation guidelines to support consistent solution delivery across business functions
• Align AI initiatives to enterprise strategies, roadmaps, and business priorities
• Identify and prioritize AI opportunities that drive student success and operational efficiency
• Design scalable AI platforms and solutions across data, application, and technology domains
• Provide guidance on how AI, ML, and automation will improve current and future-state architectures
• Lead design and implementation of AI-powered solutions (e.g., LLMs, agents, automation, and analytics)
• Integrate AI capabilities with enterprise applications and data platforms
• Guide evaluation and selection of AI/ML models, frameworks, and vendors
• Establish reusable patterns, APIs, and integration approaches
• Establish governance practices covering model oversight, responsible AI usage, security controls, compliance expectations, and lifecycle management
• Establish AI governance, including responsible AI, security, privacy, and compliance (FERPA, HIPAA, GDPR)
• Establish lifecycle management practices (MLOps/LLMOps, model monitoring, drift detection, retraining)
• Implement performance, bias, and risk monitoring frameworks
• Ensure secure AI architecture (e.g., data protection, isolation, adversarial defense)
• Partner with business, product, data, and engineering teams to provide AI solutions
• Provide architectural guidance to architects and delivery teams
• Facilitate workshops, POCs, demos and AI adoption projects identifying viable opportunities where AI can be a positive differentiator
• Mentor teams on AI best practices, patterns, and frameworks

Enterprise AI Architect

• Analyzing and modeling organizational data for the Artificial Intelligence (AI) function to draw business insights
• Applies data extraction, transformation and loading techniques in order to connect large data sets from a variety of sources
• LLM development and fine-tuning strategies, best practices, and standards to enhance AI ML model deployment and monitoring efficiency
• Develop roadmap and strategy for NLP, LLM, Gen AI model development and lifecycle implementation
• Responsible for the design and development of custom ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG)
• Collaborate closely with the MLOps, product teams, business stakeholders, machine learning engineers, and software engineers for the deployment of machine learning models into production environments
• Ensure the use of standards, governance and best practices in ML model development, and adherence to model and data governance standards.

• Build and maintain shared infrastructure: secure web app hosting infrastructure, MCP/API standards, secret/key management, model selection, guardrails, and a centralized pattern library that all builders across the organization can pull from.
• Own the review, tracking, and advisory process for all corporate AI and automation builds — MCP servers and tool configurations, Claude Skills, API integrations, and agentic workflows — before deployment.
• Partner directly with AI specialists across departments to identify, scope, and build AI and automation solutions that meet high standards for cost, security, and scalability security before they ship.
• Define and maintain the secure intake path for AI and automation requests across the organization, replacing ad-hoc builds and shadow IT with a governed, cost-effective, and scalable process.
• Contribute to the organization’s AI cost efficiency by monitoring, alerting, and setting thresholds on AI spend across usage-based tools and platforms.

AI & Automations Lead

• Own and develop enterprise accounts across the Southeast (GA, FL, TN, NC, SC, AL)
• Build 50%+ of pipeline through cold outreach, events, networking, and partner collaboration
• Manage complex enterprise sales cycles ($75K-$200K ACV, 3-9 months) with multiple stakeholders
• Sell to CIOs, COOs, CFOs, and operations leaders at manufacturers, financial institutions, and enterprise companies
• Run discovery, demos, POCs, business case development, and contract negotiation
• Partner with channel ecosystem to co-develop and accelerate opportunities
• Maintain Salesforce CRM hygiene, pipeline accuracy, and quarterly forecasting
• Travel across Southeast territory 20-40%

Enterprise Sales Executive – AI Platform

• Deploy and manage AI platforms supporting enterprise copilots, AI agents, and digital workers.
• Implement and maintain Retrieval-Augmented Generation (RAG) infrastructure.
• Deploy and manage vector databases and enterprise knowledge systems.
• Develop and maintain integrations with RegScale APIs and other enterprise platforms.
• Integrate AI services with Microsoft Copilot Studio, Power Platform, Azure AI Services, OpenAI, Anthropic, and related technologies.
• Develop automation workflows using Power Automate and API-first architectures.
• Design and maintain cloud-native infrastructure supporting AI and automation workloads.
• Manage Kubernetes, Docker, CI/CD pipelines, and Infrastructure as Code.
• Implement secure deployment pipelines for regulated environments.
• Support development of compliance automation workflows for evidence collection, POA&M management, and continuous monitoring.
• Monitor AI platform performance, observability, logging, and operational health.

Principal AI Engineer

• Lead the development of advanced AI and machine learning systems
• Design end-to-end AI solutions, optimize model performance
• Mentor junior engineers and support cross-team collaboration
• Document architectural decisions and best practices

Senior AI Engineer

• Lead the ideation, development, and productionalization of sophisticated AI solutions tailored for healthcare payer use cases.
• Architect, design, and implement complex, state-of-the-art AI models and algorithms, leveraging Python and cloud platforms like GCP Vertex AI.
• Partner strategically with product teams and business stakeholders to define and prioritize opportunities for applying advanced AI techniques to solve critical business challenges and drive process automation.
• Conduct in-depth research and maintain expertise in the latest advancements in AI, championing the adoption of cutting-edge techniques and methodologies within the team.
• Architect and develop robust, scalable, and efficient systems and code bases for deploying AI models into production environments, ensuring high availability and performance.
• Serve as a key technical advisor to our internal Lines-of-Businesses, understanding complex needs, gathering requirements, and translating them into impactful, actionable AI solutions.
• Define and oversee the evaluation and validation frameworks for AI models, ensuring their accuracy, reliability, fairness, and performance exceed business requirements.
• Mentor and guide junior engineers on best practices in AI development, model implementation, and system design.

AI Engineer jobs in Tennessee

See all jobs on JobTailor

AI Legal Engineer

Applied Artificial Intelligence Engineer

AI Workflow Engineer

Senior Software Engineer – Applied AI

Senior Engineer, AI Agentic – Transformation

Product Engineer – AI

Senior Machine Learning Engineer, AI Recommendation – Search

AI Engineer

Enterprise AI Architect

AI Engineer

AI & Automations Lead

Enterprise Sales Executive – AI Platform

Principal AI Engineer

Senior AI Engineer

Senior AI Engineer