AI Platform Engineer

Bright Vision Technologies•Alpharetta, GA

16h•$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 and enterprise workloads. The role focuses on LLM serving, distributed systems, GPU optimization, Kubernetes, cloud-native infrastructure, and MLOps, with an emphasis on performance, reliability, security, observability, and cost efficiency. The engineer will collaborate with AI researchers, ML engineers, DevOps teams, and software engineers to deploy and support production AI models, and will contribute technical leadership through mentoring and architecture reviews. The posting contains two versions of the role. One lists 6+ years of experience and a salary range of $100,000–$150,000 per annum; the more detailed version lists 10+ years of experience and a salary range of $130,000–$180,000 annually, based on experience. Both specify full-time, direct W2 employment and 100% remote work in the continental United States. The employer does not sponsor new H-1B visa petitions.

Requirements

At least 6 years of experience in distributed systems, infrastructure, or ML platform engineering; the detailed version specifies 10+ years in distributed systems, infrastructure engineering, cloud platforms, or ML platform engineering.
Bachelor’s or master’s degree in Computer Science, Computer Engineering, Artificial Intelligence, or a related technical discipline (specified in the detailed version).
Strong programming skills in Python and at least one of Go, Rust, or C++.
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.
Experience with Kubernetes, Docker, container orchestration, and cloud-native 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 tools such as Terraform, Bicep, or 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 supporting large-scale AI APIs.

Responsibilities

Design, build, and maintain scalable AI inference and model-serving platforms for production environments.
Architect highly available, cloud-native infrastructure for 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.
Implement model deployment, versioning, rollback, and lifecycle management using MLOps practices.
Develop monitoring, observability, logging, distributed tracing, and alerting solutions.
Implement caching, 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.
Optimize cloud infrastructure and resource utilization, support FinOps initiatives, and promote operational excellence.
Mentor engineering teams, conduct architecture reviews, and establish AI platform engineering and cloud-native development best practices.
Evaluate AI infrastructure technologies, model-serving frameworks, and GPU acceleration techniques.
Optimize inference performance, GPU utilization, and request routing; build autoscaling, deployment, and monitoring solutions.