Note: The job is a remote job and is open to candidates in USA. Bright Vision Technologies is seeking an AI Platform Engineer to design, build, and operate enterprise-scale AI inference and machine learning platforms. The role focuses on scalable, reliable, secure, and cost-efficient production AI infrastructure, including LLM serving, GPU optimization, Kubernetes, cloud infrastructure, and MLOps.

Responsibilities

• 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
• Collaborate with ML teams to deploy and support production AI models
• Design, build, and maintain scalable AI inference and model-serving platforms for enterprise production environments
• Architect highly available, cloud-native infrastructure supporting Large Language Models (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

Skills

• 6+ years of experience in distributed systems, infrastructure, or ML platform engineering
• Strong proficiency in Python and Go, Rust, or C++
• Experience with LLM inference frameworks (vLLM, TensorRT-LLM), Kubernetes, cloud platforms, and GPU optimization
• 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
• Strong programming skills in Python and at least one systems programming language such as Go, Rust, or C++
• Extensive experience with Large Language Model (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
• 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

Benefits

• 100% Remote (Continental United States)
• Tremendous career growth potential

Company Overview