AI Platform Engineer

Bright Vision Technologies•Nashville, TN

3d•$130,000 - $180,000•Remote

About The Position

Bright Vision Technologies is seeking an AI Platform Engineer to design, build, and operate enterprise-scale AI inference and machine learning platforms for production workloads. The role focuses on scalable, reliable, secure, and cost-efficient AI platforms, with expertise in distributed systems, LLM serving, GPU optimization, Kubernetes, cloud infrastructure, and MLOps. The position is 100% remote within the continental United States and is full-time, direct W2 employment. The posting contains two experience and salary descriptions: one lists 6+ years and $100,000–$150,000 per annum; a later description lists 10+ years and $130,000–$180,000 annually, based on experience. The engineer will architect cloud-native infrastructure for LLMs, foundation models, and machine learning services; optimize inference performance and GPU resources; establish deployment, autoscaling, monitoring, security, and high-availability practices; and collaborate across AI research, ML, DevOps, and software engineering teams. The role also includes technical leadership, mentoring, architecture reviews, cloud cost optimization, and evaluation of emerging AI infrastructure technologies.

Requirements

10+ years of professional experience in distributed systems, infrastructure engineering, cloud platforms, or machine learning platform engineering; the posting also includes an earlier description requiring 6+ years.
Bachelor's or Master's degree in Computer Science, Computer Engineering, Artificial Intelligence, or a related technical discipline.
Strong programming skills in Python and at least one systems programming language such as Go, Rust, or C++.
Extensive experience with LLM serving, model inference optimization, and production AI infrastructure.
Hands-on experience with vLLM, TensorRT-LLM, Triton Inference Server, Ray Serve, or similar AI serving frameworks.
Strong expertise in Kubernetes, container orchestration, Docker, and cloud-native application architectures.
Experience optimizing GPU workloads using CUDA, NVIDIA GPU technologies, distributed inference, and high-performance AI infrastructure.
Experience with AWS, Microsoft Azure, or Google Cloud Platform.
Strong understanding of distributed systems, networking, scalability, observability, and security best practices.
Excellent analytical, communication, collaboration, and technical leadership skills.

Nice To Haves

Experience designing and operating multi-region AI platforms and globally distributed inference services.
Knowledge of model optimization techniques such as quantization, pruning, compression, speculative decoding, KV cache optimization, and mixed-precision inference.
Experience with MLOps, GitOps, Infrastructure as Code (Terraform, Bicep, CloudFormation), and CI/CD automation.
Familiarity with service mesh technologies such as Istio or Linkerd, API gateways, and event-driven architectures.
Contributions to open-source AI infrastructure projects, technical publications, patents, or conference presentations.
Experience implementing FinOps strategies, cloud cost optimization, and enterprise AI governance.
Experience with multi-region AI deployments and AI infrastructure.
Familiarity with model optimization techniques such as quantization or compression.
Open-source contributions or experience supporting large-scale AI APIs.

Responsibilities

Design, build, and maintain scalable AI inference and model-serving platforms for enterprise production environments.
Architect highly available, cloud-native infrastructure supporting LLMs, foundation models, and machine learning services.
Optimize inference latency, throughput, GPU utilization, memory management, and request scheduling across distributed AI workloads.
Design autoscaling, workload orchestration, traffic management, and intelligent request-routing strategies for AI services.
Implement model deployment, versioning, rollback, and lifecycle management using MLOps practices.
Develop monitoring, observability, logging, distributed tracing, and alerting solutions to ensure platform reliability and performance.
Implement caching strategies, API gateways, security controls, authentication, authorization, and high-availability architectures.
Collaborate with AI researchers, ML engineers, DevOps teams, and software engineers to deploy and support production AI models.
Drive cloud infrastructure optimization, resource utilization, FinOps initiatives, and operational excellence.
Mentor engineering teams, conduct architecture reviews, and establish best practices for AI platform engineering and cloud-native development.
Evaluate emerging AI infrastructure technologies, model-serving frameworks, and GPU acceleration techniques.
Design and maintain scalable AI model-serving platforms; optimize inference performance, GPU utilization, and request routing; build autoscaling, deployment, and monitoring solutions; implement caching, security, and high-availability strategies; and collaborate with ML teams to support production AI models.