AI Platform Engineer

Bright Vision Technologies•Nashville, TN

23h•$130,000 - $180,000•Remote

About The Position

Bright Vision Technologies is seeking an AI Platform Engineer to design, build, and operate scalable AI inference and machine-learning platforms for production workloads. The role focuses on enterprise-scale, cloud-native infrastructure for LLMs, foundation models, and machine-learning services, with emphasis on performance, reliability, security, observability, and cost efficiency. The position is 100% remote within the continental United States and is full-time, direct W2 employment. The engineer will work with AI researchers, ML engineers, DevOps teams, and software engineers to deploy and support production AI models. The role also includes technical leadership through mentoring, architecture reviews, and establishing engineering practices. The posting contains two sets of compensation and experience details: an initial range of $100,000–$150,000 per annum with 6+ years of experience, and a later range of $130,000–$180,000 annually based on experience with 10+ years required. Applicants may be U.S. citizens, Green Card holders, EAD holders, or candidates transferring an H-1B. The employer states that it cannot sponsor new H-1B visa petitions for this position.

Requirements

Bachelor's or Master's degree in Computer Science, Computer Engineering, Artificial Intelligence, or a related technical discipline.
10+ years of professional experience in distributed systems, infrastructure engineering, cloud platforms, or machine-learning platform engineering; an earlier section of the posting states 6+ years.
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 cloud platforms including AWS, Microsoft Azure, or Google Cloud Platform (GCP).
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 modern 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 to drive continuous innovation.