Forward Deployed Engineer

About the Team:

SeaCloud helps customers build and run AI workloads through serverless model APIs, dedicated model hosting, GPU virtual machines, and bare metal GPU infrastructure, and adopt enterprise AI applications for collaboration and workflow automation.

As a Forward Deployed Engineer, you will work directly with customer engineers to turn these capabilities into working applications and production workloads. You will understand customer goals, configure and integrate the appropriate services, troubleshoot technical blockers, and help customers operate successfully after launch.

Your work will span API integration, model evaluation, GPU workload deployment, performance validation, and customer onboarding. For customer-managed environments, you may also help configure Kubernetes, storage access, monitoring, and supporting software within an agreed delivery scope.

This role combines hands-on engineering with customer empathy, clear communication, and ownership of practical outcomes.

Job Description:

Work Side by Side with Customers

• Understand customers business goals, workloads, technical constraints, budgets, security requirements, and operational capabilities.
• Listen carefully, confirm your understanding, and translate broad requests into measurable success criteria and actionable technical steps.
• Explain recommendations and trade-offs clearly to customer engineers and business stakeholders.
• Maintain clear updates on progress, risks, dependencies, next steps, and decisions needed from the customer.
  
  

Integrate and Validate AI Workloads

• Drive customer onboarding and integration of enterprise AI applications into existing IT environments, including identity and access management, enterprise systems, data sources, and business workflows. Partner with customers and internal engineering teams to resolve implementation challenges and support the transition from proof of concept to production adoption.
• Help customers adopt SeaCloud APIs, including authentication, streaming, timeouts, retries, rate limits, and usage reporting.
• Build sample applications, integration scripts, and migration guides, validating compatibility rather than assuming identical behavior across providers.
• Compare supported models using representative customer evaluation datasets, considering quality, latency, capabilities, and cost.
• Configure and validate inference workloads in dedicated or customer-managed GPU environments using supported tools such as vLLM or SGLang.
• Assist with container packaging, model storage access, networking, permissions, and Kubernetes workload configuration.
• Support agreed bare metal onboarding and acceptance activities alongside lead engineers and infrastructure teams.
  
  

Troubleshoot and Improve Production Readiness

• Investigate failures across APIs, Linux, containers, Kubernetes, GPU runtime dependencies, networking, and storage.
• Use logs, metrics, and reproducible tests to distinguish customer configuration issues from application or platform problems.
• Establish workload baselines and measure latency, throughput, error rates, GPU memory use, and resource utilization.
• Help customers understand resource sizing and consumption costs, making recommendations supported by workload evidence.
• Complete agreed readiness checks, document operational procedures, and support launch and stabilization before handing over to the designated operating team.
  
  

Make Delivery Repeatable

• Create reusable scripts, deployment templates, troubleshooting guides, and customer-facing documentation.
• Apply least-privilege access, secure credential handling, and approved customer-data practices.
• Coordinate escalations with Support, Product, Platform Engineering, and Infrastructure teams.
• Turn recurring customer problems into structured product feedback with clear impact, evidence, and proposed improvements.
  
  

Requirements:

• Typically 3+ years of relevant experience in cloud engineering, platform engineering, DevOps, SRE, MLOps, solutions engineering, or customer engineering, or equivalent demonstrated capability.
• Practical Linux troubleshooting skills and working knowledge of TCP/IP, DNS, HTTP, authentication, and storage access.
• Ability to write and debug Python and Bash scripts, use APIs, and maintain code in Git.
• Hands-on experience with Docker and Kubernetes workload deployment, including configuration, resource requests, persistent storage, logs, and common failure modes.
• Experience deploying or integrating an AI/ML application or inference service, with an understanding of GPU memory and runtime dependencies.
• A structured approach to troubleshooting, testing, documentation, and safe changes.
• Strong customer communication and empathy, including the ability to understand the customer's perspective and work jointly toward a solution.
• Ability to communicate technical findings clearly in written and spoken English.
• Willingness to work at customer sites and travel when an engagement requires it.
  
  

Useful Additional Experience

• OpenAI-compatible model APIs, model evaluation, or multilingual AI applications.
• vLLM, SGLang, PyTorch, Hugging Face, or similar tools.
• Helm, Ansible, or Terraform where supported by the target environment.
• Prometheus, Grafana, NVIDIA DCGM, or comparable observability tools.
• Multi-GPU workloads, NCCL testing, Slurm, or GPU cluster acceptance.
• ARM64 and x86 container compatibility.