Forward Deployed AI Engineer

Job description

Location: Saudi Arabia Full-time/On-site Experience: 4-7+ years Industry: Banking/Financial Services Who are we? the employer is a leading provider of AI-driven 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 have evolved into Tangram.ai · a modular, GenAI-powered platform designed for enterprises. Tangram enables organizations to assemble intelligent agents, solutions, and models like building blocks to create, scale, and adapt AI-powered capabilities quickly, securely, and accurately. It is not just a platform, it is the operating layer of GenAI in the enterprise. Role Overview We are looking for a forward-deployed AI Engineer to deploy, integrate, optimize and operate enterprise generative AI platforms within secure, indoor and air-tight banking environments. You will work closely with customer IT, infrastructure, security, engineering and AI teams to transform AI platform builds into reliable, production-ready deployments. What You'll Do • Deploy and configure NVIDIA NIMs, LLMs, the employer, 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 weights of LLM models in air-gapped environments. • Configure and select Mimicking NVIDIA GPU driver, GPU clocking, and MIG for AI workloads. • to improve LLM inference performance, including TTFT, throughput, latency, and GPU utilization. • Create and manage CI/CD and GitOps pipelines using tools such as ArgoCD and OpenShift Pipelines. • Develop and maintain Helm charts, Kustomize data, and Kubernetes configurations. • Troubleshoot deployment, infrastructure, networking, and performance issues in secure client environments. • Work directly with the customer's IT, Security and Infrastructure teams to resolve technical bottlenecks and ensure successful deployments. What we're looking for • 4+ years of experience in software engineering, DevOps, platform engineering, or related field. • 2+ years of working experience in MLOps/LLMOps. • Strong experience with Red Hat OpenShift and Kubernetes. • Hands-on experience with NVIDIA GPUs and NVIDIA GPU Driver. • Experience deploying LLMs, NVIDIA NIM, Triton or enterprise AI platforms. • Solid understanding of containers, Docker/OCI images, and Kubernetes workloads. • Experience with Helm, Kustomize, Kubernetes, and GitOps operators. • Experience with ArgoCD, OpenShift Pipelines, or similar CI/CD tools. • Understanding isolated/segregated environments and offline software deployment. • Good understanding of enterprise authentication and security frameworks such as Active Directory, LDAP, Kerberos, and O authentication. • Experience managing secure secrets using HashiCorp Vault, OpenShift Secrets, or similar technologies. • Strong troubleshooting, communication and customer facing skills. Brownie Points • Experience deploying AI platforms in banking/BFSI or highly regulated environments. • News Able to work in isolated or separate data centers. • CKA/CKAD certificate. • Experience with NVIDIA NIM, Triton Inference Server or the employer • Experience with graph databases such as Neo4j or ArangoDB. • Experience with GPU, MIG and GPU performance tuning. • Experience in large-scale LLM inference and RAG deployments

Requirements

• Machine learning and deep learning algorithms
• Python programming
• TensorFlow / PyTorch proficiency
• Data preprocessing and feature engineering
• Model deployment and monitoring on cloud platforms

Skills