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Native Americans in Philanthropy is dedicated to increasing and nurturing Indigenous representation in the philanthropic sector. With that in mind, the opportunities on our Job Board fit one of the following criteria:    

  1. The position is within a philanthropic or nonprofit organization 
  2. The position itself is philanthropic in nature and/or focuses on roles essential to or valued by the philanthropic sector i.e. fundraising, grantmaking, gift processing or development, donor relations, nonprofit management and/or administration, social justice, equity, conservation, etc.  

Please submit your job opportunity here and note that assessment and approval of submissions can take up to 48 hours.

NOTE: Positions marked as "Featured" are either Native-focused roles or based at organizations focused on Native communities.

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AI Cloud Engineer | LockedIn AI Careers

LockedIn AI New York $100,000-$150,000
Marketing/Communication Full Time

Job Details

About LockedIn AI

LockedIn AI is the #1 real-time AI interview and meeting copilot, trusted by over 1 million users worldwide. We are building a cutting-edge AI-powered career platform that helps candidates perform better in job interviews, coding assessments, and professional meetings using real-time artificial intelligence.

Our products — including Interview Copilot, Coding Assistant, Stealth Mode, Duo (Remote Assist), and more — are powered by scalable cloud infrastructure and advanced AI systems.

We are now hiring a high-impact AI Cloud Engineer to build and scale the infrastructure that powers our AI systems at global scale.

About the Role

We are looking for a cloud-native, AI-infrastructure-focused AI Cloud Engineer to design, build, and optimize the cloud systems that power LockedIn AI’s machine learning workloads and real-time inference services.

You will own the infrastructure layer behind model training, fine-tuning, deployment, and scaling — ensuring low-latency, high-performance AI systems for over 1 million users.

This role sits at the intersection of cloud engineering and AI systems architecture.

What You’ll Do

AI Cloud Infrastructure

  1. Design and build scalable cloud architecture for AI/ML workloads
  2. Manage GPU compute clusters for training and inference
  3. Optimize cloud systems on AWS, GCP, or Azure
  4. Build multi-environment setups (dev, staging, production) for AI systems

Model Serving & Inference

  1. Deploy and manage real-time LLM inference systems
  2. Optimize latency, throughput, and GPU utilization
  3. Work with tools like vLLM, Triton, TensorRT, or similar
  4. Build load balancing, failover, and auto-scaling systems

GPU & Training Systems

  1. Manage distributed training infrastructure across GPU clusters
  2. Optimize resource usage using spot instances and scheduling
  3. Configure managed AI platforms (SageMaker, Vertex AI, Azure ML)
  4. Improve performance and cost efficiency of ML workloads

Cost & Performance Optimization

  1. Monitor and reduce cloud infrastructure costs
  2. Optimize GPU usage, storage, and compute efficiency
  3. Build dashboards for cost tracking and usage insights
  4. Improve unit economics of AI inference systems

Security & Reliability

  1. Implement IAM, encryption, secrets management, and network security
  2. Ensure safe handling of AI models, data, and APIs
  3. Build scalable, fault-tolerant infrastructure systems
  4. Support compliance readiness (SOC 2, GDPR, CCPA)

What We’re Looking For

Required Experience

  1. 3+ years in cloud engineering, DevOps, or infrastructure roles
  2. Experience with production ML or AI workloads
  3. Strong background in Kubernetes and cloud platforms
  4. Experience with CI/CD and infrastructure automation
  5. Startup or high-growth environment experience preferred

Technical Skills

  1. AWS, GCP, or Azure (strong expertise required)
  2. Kubernetes (production cluster management, autoscaling, Helm)
  3. Infrastructure as Code (Terraform, Pulumi, CloudFormation)
  4. CI/CD tools (GitHub Actions, GitLab CI, ArgoCD, etc.)
  5. Observability tools (Prometheus, Grafana, Datadog, etc.)
  6. Python, Go, or Bash scripting

Preferred Qualifications

  1. Experience with LLM inference systems or GPU-heavy workloads
  2. Background in distributed training or real-time AI systems
  3. Knowledge of multi-cloud or hybrid cloud architectures
  4. Experience with AI optimization techniques (quantization, batching, etc.)
  5. Contributions to open-source infrastructure projects


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