Forward Deployed AI Engineer

Crayon Data Pvt Ltd; Riyadh; On-site; SAR 300,000 - 520,000; Full time

Job summary

Crayon Data, now Tangram.ai, seeks a Forward Deployed AI Engineer to deploy and operationalize enterprise GenAI platforms in secure, on-premise banking environments. You will collaborate with client IT, security, and infrastructure teams to deliver production-ready deployments.

The role emphasizes hands-on MLOps, OpenShift/Kubernetes, NVIDIA GPUs, and CI/CD pipelines in air-gapped settings, with a focus on reliable, scalable AI platform implementations.

Qualifications

• 4+ years of experience in Software Engineering, DevOps, Platform Engineering or a related field.
• 2+ years of hands-on experience in MLOps / LLMOps.
• Strong experience with Red Hat OpenShift and Kubernetes.
• Hands-on experience with NVIDIA GPUs and NVIDIA GPU Operator.
• Experience deploying LLMs, NVIDIA NIM, Triton or enterprise AI platforms.
• Strong understanding of containerization, Docker/OCI images and Kubernetes workloads.
• Experience with Helm, Kustomize, Kubernetes Operators and GitOps.
• Experience with ArgoCD, OpenShift Pipelines or similar CI/CD tools.
• Understanding of air-gapped / disconnected environments and offline software deployment.
• Good understanding of enterprise authentication and security frameworks such as Active Directory, LDAP, Kerberos and OAuth.
• Experience with secure secrets management using HashiCorp Vault, OpenShift Secrets or similar technologies.
• Strong troubleshooting, communication and client-facing skills.

Responsibilities

• Deploy and configure NVIDIA NIMs, LLMs, DataRobot, Graph Databases, and related AI platforms.
• Install and manage AI workloads on Red Hat OpenShift and Kubernetes environments.
• Package and deploy container images, dependencies, and LLM model weights in air-gapped environments.
• Configure and optimize NVIDIA GPU Operator, GPU time-slicing and MIG for AI workloads.
• Optimize LLM inference performance, including TTFT, throughput, latency and GPU utilization.
• Build and manage CI/CD and GitOps pipelines using tools such as ArgoCD and OpenShift Pipelines.
• Develop and maintain Helm charts, Kustomize manifests and Kubernetes configurations.
• Troubleshoot deployment, infrastructure, networking and performance issues in secure client environments.
• Work directly with client IT, Security and Infrastructure teams to resolve technical blockers and ensure successful deployments.

Skills

OpenShift

Kubernetes

LLMOps

MLOps

NVIDIA GPUs

NVIDIA GPU Operator

ArgoCD

GitOps

CI/CD

Docker

Triton

Security

Tools

ArgoCD

OpenShift Pipelines

Helm

Kustomize

Docker

Neo4j

ArangoDB

Triton Inference Server

DataRobot

NVIDIA NIM

GPU Operator

Job description

Location: Saudi ArabiaFull Time / On-site
Experience: 4–7+ years
Industry: Banking / Financial Services

Who are we?

Crayon Data is a leading provider of AI-led revenue acceleration solutions, headquartered in Singapore with a presence in India and the UAE. Founded in 2012, our mission has always been to simplify the world’s choices.

Today, we’ve evolved into Tangram.ai — a modular, GenAI-powered platform built for the enterprise. Tangram lets organizations assemble intelligent agents, solutions, and models like building blocks to create, scale, and adapt AI-powered capabilities with speed, security, and precision. It’s not just a platform,it’s the operating layer for GenAI in the enterprise.

Role Overview

We are looking for a Forward Deployed AI Engineer to deploy, integrate, optimize, and operationalize enterprise Generative AI platforms within secure, on-premise and air-gapped banking environments.

You will work closely with client IT, infrastructure, security, engineering, and AI teams to turn AI platform architectures into reliable, production-ready deployments.

What You'll Do

• Deploy and configure NVIDIA NIMs, LLMs, DataRobot, Graph Databases, and related AI platforms.
  
• Install and manage AI workloads on Red Hat OpenShift and Kubernetes environments.
  
• Package and deploy container images, dependencies, and LLM model weights in air-gapped environments.
  
• Configure and optimize NVIDIA GPU Operator, GPU time-slicing and MIG for AI workloads.
  
• Optimize LLM inference performance, including TTFT, throughput, latency and GPU utilization.
  
• Build and manage CI/CD and GitOps pipelines using tools such as ArgoCD and OpenShift Pipelines.
  
• Develop and maintain Helm charts, Kustomize manifests and Kubernetes configurations.
  
• Troubleshoot deployment, infrastructure, networking and performance issues in secure client environments.
  
• Work directly with client IT, Security and Infrastructure teams to resolve technical blockers and ensure successful deployments.
  

What We're Looking For

• 4+ years of experience in Software Engineering, DevOps, Platform Engineering or a related field.
  
• 2+ years of hands‑on experience in MLOps / LLMOps.
  
• Strong experience with Red Hat OpenShift and Kubernetes.
  
• Hands‑on experience with NVIDIA GPUs and NVIDIA GPU Operator.
  
• Experience deploying LLMs, NVIDIA NIM, Triton or enterprise AI platforms.
  
• Strong understanding of containerization, Docker/OCI images and Kubernetes workloads.
  
• Experience with Helm, Kustomize, Kubernetes Operators and GitOps.
  
• Experience with ArgoCD, OpenShift Pipelines or similar CI/CD tools.
  
• Understanding of air‑gapped / disconnected environments and offline software deployment.
  
• Good understanding of enterprise authentication and security frameworks such as Active Directory, LDAP, Kerberos and OAuth.
  
• Experience with secure secrets management using HashiCorp Vault, OpenShift Secrets or similar technologies.
  
• Strong troubleshooting, communication and client‑facing skills.
  

Brownie Points

• Experience deploying AI platforms in Banking / BFSI or highly regulated environments.
  
• Experience working in air‑gapped or disconnected data centers.
  
• CKA / CKAD certification.
  
• Experience with NVIDIA NIM, Triton Inference Server or DataRobot.
  
• Experience with Graph Databases such as Neo4j or ArangoDB.
  
• Experience with GPU performance tuning, MIG and GPU time‑slicing.
  
• Experience with large‑scale LLM inference and RAG deployments.