AI Platform Engineer

Bright Vision Technologies•Alpharetta, GA

4d•$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 infrastructure, including LLM serving, GPU optimization, Kubernetes, cloud-native systems, distributed systems, and MLOps. The position is 100% remote within the continental United States and is listed as full-time, direct W2. The posting contains differing experience and salary details: an initial listing specifies 6+ years and $100,000–$150,000 per annum, while the later listing specifies 10+ years and $130,000–$180,000 annually, based on experience. The engineer will collaborate with AI researchers, ML engineers, DevOps teams, and software engineers to deploy and support production models; improve platform performance and reliability; and guide engineering practices through architecture reviews and mentoring. Bright Vision Technologies describes itself as a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. The employer states that it cannot sponsor new H-1B petitions for this position, though U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates are encouraged to apply.

Requirements

At least 6 years of experience in distributed systems, infrastructure, or ML platform engineering; the later listing specifies 10+ years of professional experience in distributed systems, infrastructure engineering, cloud platforms, or machine learning platform engineering.
Bachelor's or Master's degree in Computer Science, Computer Engineering, Artificial Intelligence, or a related technical discipline, as stated in the later listing.
Strong programming skills in Python and at least one systems programming language such as 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.
Strong expertise in Kubernetes, container orchestration, Docker, 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.
Experience with Kubernetes, cloud platforms, and GPU optimization.

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 production environments.
Architect highly available, cloud-native infrastructure for LLMs, foundation models, and machine learning services.
Optimize inference latency and 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 for 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.
Optimize cloud infrastructure, resource utilization, FinOps initiatives, and operational practices.
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.
Support and maintain scalable AI model-serving platforms, inference performance, GPU utilization, autoscaling, deployment, monitoring, caching, security, and high availability.