AI ML Engineer Visa Sponsorship Jobs in Tennessee
Tennessee's AI/ML engineering roles are concentrated in Nashville, Memphis, and Knoxville, with employers spanning healthcare technology firms like HCA Healthcare and Change Healthcare, logistics companies, and Oak Ridge National Laboratory's growing research programs. Vanderbilt University and the University of Tennessee also generate strong local pipelines that attract international talent seeking visa sponsorship.
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Get Access To All JobsINTRODUCTION
The Principal AI Agent / ML Software Engineer is a Senior Staff-level, hands-on technical leadership role responsible for defining, building, and operating next-generation AI systems on Oracle Cloud Infrastructure (OCI). This person will set architecture and engineering direction for production-grade agentic AI platforms, autonomous workflows, scalable inference infrastructure, and enterprise AI applications used in large-scale, business-critical environments.
This role requires a proven engineer who can translate ambiguous product and platform goals into durable technical strategy, lead multi-team execution without direct authority, and remain deeply hands-on in design, code, reviews, operations, and incident follow-up. The ideal candidate combines deep distributed systems experience with practical AI-native engineering, including orchestration of LLMs, tools, APIs, memory, retrieval, evaluation, guardrails, and cloud services. The expectation is to ship, scale, and operate reliable, secure, observable, and cost-aware AI platform systems while raising the technical bar for engineers across the organization.
Responsibilities
- Serve as a senior technical owner for OCI AI platform capabilities, including agent execution, inference systems, model serving, AI workflow orchestration, evaluation, and observability.
- Design, architect, and deliver scalable agentic AI systems capable of reasoning, planning, tool use, workflow execution, multi-step task orchestration, and safe human-in-the-loop escalation.
- Build production-grade services for tool calling, agent memory, context management, Model Context Protocol (MCP) integration, vector retrieval, multi-agent coordination, policy enforcement, and evaluation.
- Lead architecture across distributed services optimized for low latency, high throughput, GPU efficiency, reliability, cost, operability, and secure multi-tenant operation.
- Define service boundaries, APIs, data models, state management, consistency tradeoffs, failure modes, SLIs/SLOs, rollout strategies, and operational readiness criteria for AI platform services.
- Drive technical strategy across infrastructure, platform, security, data, and application engineering teams, converting broad goals into executable multi-quarter plans and measurable milestones.
- Integrate AI agents securely and reliably with enterprise APIs, cloud services, databases, identity systems, secrets management, and external systems.
- Establish AgentOps and LLMOps practices for tracing, monitoring, eval suites, regression testing, experimentation, safety guardrails, prompt/tool versioning, and production reliability.
- Evaluate and operationalize emerging technologies in generative AI, agentic workflows, inference optimization, long-context systems, reasoning models, AI developer tooling, and agentic-first development.
- Drive engineering excellence through code reviews, design reviews, test strategy, deployment automation, incident analysis, documentation, and AI-assisted development practices using tools such as Codex, Claude Code, Cursor, Copilot, or similar systems.
- Mentor Staff and senior engineers, raise architectural standards, and influence engineering practices across OCI without requiring direct management authority.
- Own critical production outcomes, including reliability, performance, security posture, cost efficiency, and supportability for the systems delivered.
REQUIRED QUALIFICATIONS
- Bachelor's, Master's, or Ph.D. in Computer Science, AI/ML, Engineering, or a related field, or equivalent practical experience.
- 6-10+ years of professional software engineering experience, including significant ownership of production systems; or equivalent experience demonstrating Senior Staff / Principal-level impact.
- Proven track record as a Staff, Senior Staff, Principal, or equivalent technical leader influencing architecture and execution across multiple teams.
- Deep experience designing, building, and operating high-scale distributed systems, cloud services, infrastructure platforms, or AI/ML platform services.
- Hands-on experience with production AI systems, agentic AI applications, autonomous workflows, tool-using agents, multi-step orchestration, or multi-agent systems.
- Practical experience with orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen, LlamaIndex, or similar ecosystems.
- Deep understanding of LLM application patterns, including prompt design, structured outputs, function/tool calling, context management, RAG, memory, tool safety, and evaluation.
- Strong programming skills in Python and ability to contribute high-quality production code, reviews, tests, and debugging in complex distributed environments.
- Strong expertise with Kubernetes, Docker, cloud-native infrastructure, service-to-service communication, scalability, fault tolerance, observability, and performance analysis.
- Experience defining SLIs/SLOs, production readiness criteria, incident response practices, monitoring, tracing, experiments, and reliability programs for AI or distributed systems.
- Strong understanding of AI safety, governance, security, and operational risks for autonomous or semi-autonomous systems, including data handling, access control, auditability, and human accountability.
- Excellent written and verbal communication, with demonstrated ability to lead technical direction, resolve ambiguity, and influence senior stakeholders.
PREFERRED QUALIFICATIONS
- Experience optimizing large-scale GPU inference or training workloads for latency, throughput, utilization, availability, and cost.
- Experience building or operating model serving, inference gateways, agent runtimes, workflow engines, developer platforms, or internal AI productivity platforms.
- Experience integrating AI systems with enterprise APIs, databases, cloud services, vector databases, embeddings, retrieval systems, identity systems, and policy enforcement layers.
- Experience with LLM fine-tuning, long-context systems, reasoning models, model routing, caching, batching, quantization, or emerging generative AI research.
- Experience building evaluation frameworks for agentic systems, including offline evals, online experiments, golden tasks, adversarial testing, regression gates, and observability dashboards.
- Experience using AI-assisted software development tools such as Codex, Claude Code, Cursor, Copilot, or similar systems in large-scale engineering environments.
- Track record of defining architectural standards, platform capabilities, or engineering practices adopted across multiple teams or organizations.
- Experience in enterprise, cloud infrastructure, regulated, security-sensitive, or mission-critical environments.
INTRODUCTION
The Principal AI Agent / ML Software Engineer is a Senior Staff-level, hands-on technical leadership role responsible for defining, building, and operating next-generation AI systems on Oracle Cloud Infrastructure (OCI). This person will set architecture and engineering direction for production-grade agentic AI platforms, autonomous workflows, scalable inference infrastructure, and enterprise AI applications used in large-scale, business-critical environments.
This role requires a proven engineer who can translate ambiguous product and platform goals into durable technical strategy, lead multi-team execution without direct authority, and remain deeply hands-on in design, code, reviews, operations, and incident follow-up. The ideal candidate combines deep distributed systems experience with practical AI-native engineering, including orchestration of LLMs, tools, APIs, memory, retrieval, evaluation, guardrails, and cloud services. The expectation is to ship, scale, and operate reliable, secure, observable, and cost-aware AI platform systems while raising the technical bar for engineers across the organization.
Responsibilities
- Serve as a senior technical owner for OCI AI platform capabilities, including agent execution, inference systems, model serving, AI workflow orchestration, evaluation, and observability.
- Design, architect, and deliver scalable agentic AI systems capable of reasoning, planning, tool use, workflow execution, multi-step task orchestration, and safe human-in-the-loop escalation.
- Build production-grade services for tool calling, agent memory, context management, Model Context Protocol (MCP) integration, vector retrieval, multi-agent coordination, policy enforcement, and evaluation.
- Lead architecture across distributed services optimized for low latency, high throughput, GPU efficiency, reliability, cost, operability, and secure multi-tenant operation.
- Define service boundaries, APIs, data models, state management, consistency tradeoffs, failure modes, SLIs/SLOs, rollout strategies, and operational readiness criteria for AI platform services.
- Drive technical strategy across infrastructure, platform, security, data, and application engineering teams, converting broad goals into executable multi-quarter plans and measurable milestones.
- Integrate AI agents securely and reliably with enterprise APIs, cloud services, databases, identity systems, secrets management, and external systems.
- Establish AgentOps and LLMOps practices for tracing, monitoring, eval suites, regression testing, experimentation, safety guardrails, prompt/tool versioning, and production reliability.
- Evaluate and operationalize emerging technologies in generative AI, agentic workflows, inference optimization, long-context systems, reasoning models, AI developer tooling, and agentic-first development.
- Drive engineering excellence through code reviews, design reviews, test strategy, deployment automation, incident analysis, documentation, and AI-assisted development practices using tools such as Codex, Claude Code, Cursor, Copilot, or similar systems.
- Mentor Staff and senior engineers, raise architectural standards, and influence engineering practices across OCI without requiring direct management authority.
- Own critical production outcomes, including reliability, performance, security posture, cost efficiency, and supportability for the systems delivered.
REQUIRED QUALIFICATIONS
- Bachelor's, Master's, or Ph.D. in Computer Science, AI/ML, Engineering, or a related field, or equivalent practical experience.
- 6-10+ years of professional software engineering experience, including significant ownership of production systems; or equivalent experience demonstrating Senior Staff / Principal-level impact.
- Proven track record as a Staff, Senior Staff, Principal, or equivalent technical leader influencing architecture and execution across multiple teams.
- Deep experience designing, building, and operating high-scale distributed systems, cloud services, infrastructure platforms, or AI/ML platform services.
- Hands-on experience with production AI systems, agentic AI applications, autonomous workflows, tool-using agents, multi-step orchestration, or multi-agent systems.
- Practical experience with orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen, LlamaIndex, or similar ecosystems.
- Deep understanding of LLM application patterns, including prompt design, structured outputs, function/tool calling, context management, RAG, memory, tool safety, and evaluation.
- Strong programming skills in Python and ability to contribute high-quality production code, reviews, tests, and debugging in complex distributed environments.
- Strong expertise with Kubernetes, Docker, cloud-native infrastructure, service-to-service communication, scalability, fault tolerance, observability, and performance analysis.
- Experience defining SLIs/SLOs, production readiness criteria, incident response practices, monitoring, tracing, experiments, and reliability programs for AI or distributed systems.
- Strong understanding of AI safety, governance, security, and operational risks for autonomous or semi-autonomous systems, including data handling, access control, auditability, and human accountability.
- Excellent written and verbal communication, with demonstrated ability to lead technical direction, resolve ambiguity, and influence senior stakeholders.
PREFERRED QUALIFICATIONS
- Experience optimizing large-scale GPU inference or training workloads for latency, throughput, utilization, availability, and cost.
- Experience building or operating model serving, inference gateways, agent runtimes, workflow engines, developer platforms, or internal AI productivity platforms.
- Experience integrating AI systems with enterprise APIs, databases, cloud services, vector databases, embeddings, retrieval systems, identity systems, and policy enforcement layers.
- Experience with LLM fine-tuning, long-context systems, reasoning models, model routing, caching, batching, quantization, or emerging generative AI research.
- Experience building evaluation frameworks for agentic systems, including offline evals, online experiments, golden tasks, adversarial testing, regression gates, and observability dashboards.
- Experience using AI-assisted software development tools such as Codex, Claude Code, Cursor, Copilot, or similar systems in large-scale engineering environments.
- Track record of defining architectural standards, platform capabilities, or engineering practices adopted across multiple teams or organizations.
- Experience in enterprise, cloud infrastructure, regulated, security-sensitive, or mission-critical environments.
AI ML Engineer Job Roles in Tennessee
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Search AI ML Engineer Jobs in TennesseeAI ML Engineer Jobs in Tennessee: Frequently Asked Questions
Which companies in Tennessee sponsor visas for AI/ML engineers?
Healthcare technology is the dominant sponsoring sector in Tennessee. HCA Healthcare, Change Healthcare (now part of Optum), and Vanderbilt University Medical Center have all filed H-1B visa petitions for machine learning and data science roles. Oak Ridge National Laboratory sponsors researchers under both H-1B and J-1 visa categories. Logistics and supply chain firms headquartered in Memphis, including FedEx, also have a track record of sponsoring AI and ML engineering positions.
Which visa types are most common for AI/ML engineer roles in Tennessee?
The H-1B is the most common visa category for AI/ML engineers in Tennessee, as the role consistently qualifies as a specialty occupation requiring at least a bachelor's degree in computer science, mathematics, or a related field. Researchers at institutions like Oak Ridge National Laboratory may also work under O-1 visa or J-1 visas. Candidates with Canadian or Mexican citizenship may qualify for the TN visa under the NAFTA/USMCA computer systems analyst category.
Which cities in Tennessee have the most AI/ML engineer visa sponsorship jobs?
Nashville leads the state for AI/ML sponsorship activity, driven by its large healthcare IT sector and a growing startup ecosystem. Knoxville is notable for research-oriented roles tied to Oak Ridge National Laboratory and the University of Tennessee. Memphis sees sponsorship mainly through large logistics and operations firms. Smaller clusters exist in Chattanooga, which has invested in smart infrastructure and technology initiatives attracting engineering talent.
How to find ai ml engineer visa sponsorship jobs in Tennessee?
Migrate Mate is designed specifically for international job seekers and filters AI/ML engineer roles in Tennessee by visa sponsorship history, so you are not wasting time on employers unlikely to sponsor. The platform surfaces positions from healthcare tech, research institutions, and logistics firms active in Nashville, Knoxville, and Memphis. Filtering by role and state on Migrate Mate gives you a focused list of employers with a verified sponsorship track record in this field.
Are there any state-specific factors AI/ML engineers should know before pursuing sponsorship in Tennessee?
Tennessee has no state income tax on wages, which affects overall compensation packages and sometimes influences how employers structure offers, though H-1B prevailing wage requirements set by the Department of Labor still apply regardless of state tax policy. The Oak Ridge corridor creates a concentration of research-focused roles that may require security clearance eligibility, which can limit sponsorship options for some international candidates. Vanderbilt and UT Knoxville alumni networks are active hiring pipelines worth engaging early.
What is the prevailing wage for sponsored ai ml engineer jobs in Tennessee?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.