LockedIn AI is a leading real-time AI interview and meeting copilot trusted by more than one million users worldwide.

Our platform provides real-time, AI-powered assistance during live interviews, coding assessments, professional meetings, and career preparation. We help candidates communicate with greater clarity, confidence, and competence during important career opportunities.

We are building one of the most advanced AI-powered career platforms on the market—and we are looking for an experienced AI Cloud Engineer to help scale the infrastructure behind it.

About the Role

We are seeking a cloud-native, AI-infrastructure-focused AI Cloud Engineer to design, build, operate, and optimize the cloud environments that power LockedIn AI’s machine learning workloads, real-time inference systems, and AI-driven product features.

This specialized role sits at the intersection of cloud engineering, DevOps, MLOps, and artificial intelligence infrastructure.

You will own the cloud infrastructure supporting the complete AI lifecycle, including:

Model training and fine-tuning

Model evaluation and experimentation

Real-time inference and model serving

GPU resource management

AI data pipelines

Cloud cost optimization

Infrastructure security and compliance

Monitoring, observability, and automation

The ideal candidate understands both cloud infrastructure and the unique operational demands of AI systems. You know how to architect secure cloud environments, manage Kubernetes clusters, optimize GPU usage, reduce inference latency, and control infrastructure costs at scale.

Key Responsibilities

AI-Optimized Cloud Architecture

Design and architect scalable cloud infrastructure specifically for AI and machine learning workloads.

Build GPU compute clusters for model training, fine-tuning, evaluation, and production inference.

Design cloud environments using AWS, Google Cloud Platform, or Microsoft Azure.

Select appropriate GPU instances, memory configurations, networking, and storage solutions for demanding AI workloads.

Create isolated training, evaluation, staging, and production environments with appropriate security boundaries.

Implement elastic scaling for inference endpoints and dynamically provision resources for training workloads.

Automatically scale down idle infrastructure to reduce unnecessary cloud spending.

Model Serving and Inference Infrastructure

Build and operate production infrastructure for real-time AI responses.

Support LLM inference endpoints, speech-to-text systems, retrieval-augmented generation pipelines, and other AI-powered services.

Deploy and manage model-serving frameworks such as:

vLLM

NVIDIA Triton Inference Server

TensorRT

Text Generation Inference

Similar model-serving technologies

Optimize inference systems for latency, throughput, availability, and concurrent request handling.

Implement GPU memory optimization, batching, request routing, KV-cache management, and model parallelism.

Design load balancing, traffic routing, failover, and disaster recovery mechanisms for AI endpoints.

Maintain reliable, low-latency AI services for a platform serving more than one million users.

GPU Compute and Training Infrastructure

Provision and manage cloud-based GPU environments for model training, fine-tuning, and evaluation.

Configure GPU clusters, distributed training systems, job scheduling, and resource allocation.

Implement spot or preemptible instance strategies to reduce training costs.

Manage job queues, multi-tenant GPU scheduling, automatic cleanup, and resource utilization.

Support data-parallel and model-parallel training across multiple GPUs and nodes.

Configure and manage AI cloud services such as Amazon SageMaker, Google Vertex AI, Azure Machine Learning, or equivalent platforms.

Cloud Cost Optimization and FinOps

Own cloud cost monitoring and optimization across AI infrastructure.

Analyze spending across GPU compute, storage, networking, managed AI services, and third-party model providers.

Implement reserved-instance planning, spot-instance utilization, idle-resource detection, and infrastructure right-sizing.

Build dashboards and alerts for:

Cost per training job

Cost per inference request

GPU utilization

Cloud infrastructure spending

Resource efficiency

Work with AI engineering teams to analyze token usage and model-routing efficiency.

Implement caching, request deduplication, and other strategies to reduce LLM API costs.

Balance infrastructure performance, reliability, and cost efficiency.

Networking, Security, and Compliance

Design secure cloud networking for latency-sensitive AI workloads.

Configure VPCs, private endpoints, network segmentation, and high-bandwidth inter-node communication.

Implement identity and access management policies for AI-related resources.

Protect model weights, embeddings, training data, vector databases, APIs, and other sensitive AI assets.

Implement encryption at rest and in transit, secrets management, and audit logging.

Support the organization’s progress toward SOC 2, GDPR, and CCPA readiness.

Establish secure data-handling and access-control policies across AI infrastructure.

Infrastructure as Code and Automation

Manage cloud infrastructure using Infrastructure as Code tools such as Terraform, Pulumi, or CloudFormation.

Ensure infrastructure changes are version-controlled, reproducible, peer-reviewed, and auditable.

Build automated provisioning workflows for training environments, evaluation clusters, and inference endpoints.

Create self-service infrastructure tools for AI and machine learning teams.

Implement GitOps workflows and reliable deployment pipelines.

Monitoring and Observability

Build monitoring systems for GPU health, inference performance, network throughput, storage performance, and cloud availability.

Track inference latency, request failures, GPU utilization, memory usage, and service reliability.

Use observability platforms such as Prometheus, Grafana, Datadog, CloudWatch, or similar tools.

Implement alerts for:

GPU failures

Inference latency spikes

Training job failures

Storage exhaustion

Infrastructure outages

Unexpected cost increases

Diagnose and resolve infrastructure performance issues across production AI systems.

Required Qualifications

Experience

Three or more years of experience in cloud engineering, DevOps, infrastructure engineering, platform engineering, or a related role.

Demonstrated experience managing cloud infrastructure for AI or machine learning workloads.

Experience supporting model training, fine-tuning, evaluation, and production inference.

Hands-on experience with GPU computing environments.

Experience with AI-specific cloud services such as SageMaker, Vertex AI, Azure Machine Learning, or similar platforms.

Experience collaborating with AI engineers, machine learning engineers, MLOps teams, and platform teams.

Ability to work independently in a fast-moving startup or high-growth environment.

Education

Bachelor’s degree in Computer Science, Cloud Computing, Systems Engineering, Software Engineering, or a related discipline.

Equivalent practical experience may also be considered.

Relevant certifications are beneficial, including:

AWS Certified Solutions Architect

AWS Certified Machine Learning – Specialty

Google Cloud Professional Machine Learning Engineer

Microsoft Certified Azure AI Engineer

Certified Kubernetes Administrator

Certified Kubernetes Security Specialist

Technical Skills

Strong proficiency in Python, Go, Bash, or a similar language for infrastructure automation and operational tooling.

Deep experience with at least one major cloud platform: AWS, GCP, or Azure.

Knowledge of GPU instance families, high-performance computing environments, and AI-focused cloud services.

Strong Kubernetes experience, including:

Cluster management

GPU scheduling

Helm charts

Pod autoscaling

Workload orchestration

AI model-serving deployments

Experience with inference technologies such as vLLM, Triton Inference Server, TGI, TensorRT, or similar tools.

Strong knowledge of Terraform, Pulumi, CloudFormation, or another Infrastructure as Code platform.

Experience with GitOps and automated deployment workflows.

Experience with monitoring and observability tools such as Prometheus, Grafana, Datadog, or CloudWatch.

Understanding of GPU-specific performance metrics and inference monitoring.

Professional Skills

Strong understanding of the unique infrastructure requirements of AI systems.

Ability to design infrastructure around GPU memory constraints, latency requirements, scalability, and high availability.

Cost-conscious engineering mindset with a focus on infrastructure efficiency.

Excellent written and verbal communication skills.

Ability to clearly document architecture, infrastructure decisions, operational procedures, and technical tradeoffs.

Strong ownership mentality and the ability to manage cloud systems from design through production operations.

Comfortable working autonomously, identifying problems, and implementing improvements without extensive supervision.

Preferred Qualifications

Experience operating large-scale LLM inference infrastructure.

Experience with multi-GPU model serving and model parallelism.

Experience managing high-concurrency inference systems for consumer-facing products.

Background in distributed training across multiple GPUs or nodes.

Knowledge of NCCL configuration and large-scale training storage optimization.

Experience with WebSocket infrastructure, audio-processing pipelines, or real-time streaming systems.

Familiarity with RDMA, InfiniBand, or high-bandwidth GPU interconnects.

Experience implementing multi-cloud or hybrid-cloud infrastructure.

Experience working in career technology, education technology, SaaS, or B2C software.

Contributions to open-source AI infrastructure or cloud engineering projects.

Published technical writing, conference presentations, or public engineering work.

Previous experience as an early-stage startup employee or founder.

What We Offer

Meaningful Equity

Receive early-stage equity and become a genuine co-owner of what we are building.

Direct Impact

Your work will power AI products used by more than one million people worldwide.

High-Ownership Environment

Join a lean, ambitious team where every employee has meaningful responsibility and influence.

Remote-First Flexibility

Work remotely from anywhere within the United States, with optional hybrid collaboration in New York City.

Professional Growth

Build advanced AI cloud infrastructure at a rapidly growing company entering its next stage of development.

Fast-Moving Culture

Work in a user-focused, feedback-driven environment where teams build, test, learn, and improve quickly.

Why Join LockedIn AI?

Build a Category-Defining Product

LockedIn AI pioneered the dual-layer AI copilot and human-assistance model for interviews.

Work in a Rapidly Growing Market

The AI-powered career tools market is expanding quickly, and LockedIn AI already serves more than one million users.

Own the AI Cloud Infrastructure

You will have end-to-end ownership of the infrastructure supporting our AI products, with direct influence on model performance, cost, reliability, and scalability.

Work in an AI-Native Environment

You will work at the forefront of applied artificial intelligence and help shape products built using advanced models and emerging technologies.

Ship Meaningful Work

Infrastructure decisions happen quickly, giving you the opportunity to design and deploy systems with immediate, measurable impact.

How to Apply

Please submit the following:

Your resume or CV

A brief note explaining why you would like to join LockedIn AI

Whether you have tried the LockedIn AI platform

Your thoughts on what could be improved

Relevant portfolio projects, GitHub repositories, architecture examples, or technical writing

Portfolio materials are optional but strongly encouraged.

Equal Opportunity Employment

LockedIn AI is committed to building a diverse, inclusive, and supportive workplace.

We welcome applicants from all backgrounds, identities, and experiences. Employment decisions are based on qualifications, experience, merit, and business requirements.

Ready to help build the cloud infrastructure behind the future of AI-powered career support? Apply now.