AI Cloud Engineer

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 next-generation AI career platform that helps users succeed in interviews, coding assessments, and professional communication using real-time AI assistance.

Our system runs on complex, high-performance AI infrastructure that must scale reliably, globally, and in real time.

Role Overview

We are hiring a cloud-native AI Cloud Engineer to design, build, and optimize the infrastructure powering LockedIn AI’s machine learning systems and real-time AI products.

This is a specialized role at the intersection of cloud engineering, distributed systems, and AI infrastructure. You will be responsible for the environments where models are trained, fine-tuned, deployed, and served at scale to over 1 million users.

You will own the full AI cloud stack — from GPU compute clusters to inference serving infrastructure and cost-optimized scaling systems.

Key Responsibilities

AI Cloud Architecture

• Design cloud-native infrastructure for AI/ML workloads
• Build GPU clusters for training, fine-tuning, and evaluation
• Architect multi-environment setups (training, staging, production)
• Optimize AWS/GCP/Azure systems for AI performance

Inference & Model Serving Infrastructure

• Deploy and manage real-time AI inference systems (LLMs, STT, RAG)
• Optimize serving frameworks like vLLM, Triton, TensorRT, or TGI
• Improve latency, throughput, batching, and GPU utilization
• Build failover, routing, and load balancing for AI endpoints

GPU & Distributed Compute

• Manage GPU infrastructure for distributed training and inference
• Configure multi-node training and model parallelism
• Optimize scheduling for spot and reserved GPU instances
• Automate scaling of compute resources based on demand

Cloud Cost Optimization (FinOps for AI)

• Reduce cloud spend across GPU, storage, and inference workloads
• Implement reserved/spot instance strategies for AI training
• Track cost-per-inference and cost-per-training job metrics
• Optimize LLM API usage and caching strategies

Infrastructure as Code & Automation

• Build all infrastructure using Terraform, Pulumi, or CloudFormation
• Automate provisioning of AI environments and services
• Implement GitOps workflows for cloud infrastructure
• Ensure reproducible and version-controlled cloud systems

Observability & Reliability

• Monitor GPU health, inference latency, and system performance
• Build dashboards for AI infrastructure metrics
• Set up alerting for failures, spikes, and performance degradation
• Ensure high availability of real-time AI systems

Security & Networking

• Design secure cloud networks (VPC, IAM, encryption, access control)
• Protect model weights, embeddings, and AI pipelines
• Ensure compliance readiness (SOC2, GDPR, CCPA)
• Secure inference endpoints and data flows

Required Qualifications

Experience

• 3+ years in cloud engineering, DevOps, or infrastructure roles
• Experience with ML/AI production systems
• Hands-on GPU infrastructure or AI deployment experience
• Experience working with engineering and AI teams in production environments

Technical Skills

• Cloud platforms: AWS, GCP, or Azure (deep expertise)
• Kubernetes + Docker (production-grade usage)
• Infrastructure as Code: Terraform / Pulumi / CloudFormation
• AI serving frameworks: vLLM, Triton, TensorRT, or similar
• Monitoring tools: Prometheus, Grafana, Datadog, CloudWatch
• Python / Go / Bash for automation and tooling

Preferred Qualifications

• Experience with LLM inference at scale
• Distributed training systems (multi-GPU / multi-node)
• Real-time systems (WebSockets, streaming, low-latency APIs)
• Knowledge of RDMA, InfiniBand, or GPU networking
• Multi-cloud infrastructure experience
• Background in SaaS, edtech, or AI consumer products

What We Offer

• Equity in a fast-growing AI company
• Direct impact on a product used by 1M+ users
• Remote-first flexibility with optional NYC collaboration
• High ownership over AI infrastructure systems
• Fast-paced startup environment with real technical challenges

Why Join LockedIn AI?

• Build infrastructure powering real-time AI at scale
• Work on GPU-heavy, latency-critical AI systems
• Own cloud systems that directly impact model performance
• Join a category-defining AI career tools platform
• Operate at the frontier of applied AI infrastructure

How to Apply

Please submit:

• Resume / CV
• Short note including:
  • Why you want to join LockedIn AI
  • Whether you’ve used the product
  • What improvements you would suggest
• GitHub, projects, or technical writing (optional)

Equal Opportunity

LockedIn AI is committed to building a diverse and inclusive team. All hiring decisions are based on merit, skills, and business needs.