SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
FriendliAI is seeking a Forward Deployed Engineer to work directly with enterprise customers, helping them deploy, scale, and operate generative and agentic AI workloads on FriendliAI's inference infrastructure. This is a hands-on, customer-embedded role where you will collaborate with customer engineering teams to solve production AI inference challenges.
You will design and implement large-scale deployment architectures for LLM and multimodal inference, deploy and manage containerized workloads across Kubernetes clusters, and diagnose production issues such as performance bottlenecks. Key responsibilities include integrating FriendliAI's infrastructure into customers' CI/CD workflows, developing deployment automation (scripts, Helm charts, Terraform modules), contributing field insights to improve platform reliability and observability, and leading technical workshops to help customers master infrastructure best practices.
FriendliAI's products include Serverless Endpoints, Dedicated Endpoints, and Friendli Container—a service that allows customers to download the inference engine as Docker images for deployment in private clouds or on-premises environments, with native AWS EKS integration via an EKS add-on.
The company describes itself as building the fastest inference cloud for agents, delivering up to 7x faster output token speed, up to 90% lower inference costs, and 99.99% uptime. They focus on demanding agent workloads including long-context inference, real-time streaming, and accurate tool calling.
QUALIFICATIONS:
- 3+ years of experience in cloud infrastructure, DevOps, or reliability engineering
- Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent
- Proficiency with Kubernetes, Docker, Terraform, and Helm
- Strong foundation in distributed systems, networking, and performance tuning
- Experience with GPU-based computing and generative AI model serving workloads
- Strong technical background in backend systems or AI tooling
- Experience operating workloads on AWS, GCP, or OCI
- Excellent problem-solving and debugging skills in real-world environments
PREFERRED EXPERIENCE:
- Experience deploying large models (LLMs, diffusion models) on GPUs or clusters
- Familiarity with inference frameworks (Triton, vLLM, TensorRT, DeepSpeed-Inference)
- Familiarity with observability stacks (Prometheus, Grafana, Loki, ELK, OTEL)
- Understanding of networking security and compliance frameworks (e.g., SOC 2)
- Experience supporting on-prem or hybrid-cloud deployments
