Lead AI Application Engineer – Infrastructure, LLMOps

TechBiz Global

Lead AI Application Engineer at TechBiz Global providing AI platform management and developer tools for top clients. Focused on infrastructure, data services, and model deployment.

Posted 6/19/2026full-timeRemote • 🇪🇸 SpainSeniorWebsite

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Hard Skills

KubernetesDockerTerraformPulumiAWSAzureGCPPythonGoRust

Soft Skills

communicationleadershiporganizational

Tools & Technologies

NVIDIA AI EnterpriseOpenShiftvLLMTGINVIDIA TritonVector DatabasesSQLNoSQLSelf-Service PortalCLI

Industry Keywords

AI PlatformML lifecycleMLOpsInference-as-a-ServiceEmbeddings-as-a-ServiceRAG-as-a-ServiceAI Data InfrastructureModel HostingInternal Developer PlatformsTime-to-Inference

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoGoogle Cloud PlatformKubernetesNoSQLOpenShiftPythonRustSQLTerraform

About the role

Key responsibilities & impact

Build & Run the Shared AI Platform
Architect and maintain a multi-tenant AI Platform that supports the full ML lifecycle across cloud and on-premises environments.
Ensure high availability, low latency, and cost-efficiency for all shared AI resources.
Implement LLMOps/MLOps best practices, including automated deployment pipelines for models.
Curate the AI Services Catalogue
Develop and expose "as-a-service" capabilities: Inference-as-a-Service, Embeddings-as-a-Service, and RAG-as-a-Service.
Standardize how squads interact with LLMs, providing unified APIs and abstraction layers to prevent vendor lock-in.
Manage AI Data Infrastructure
Own the deployment and scaling of Vector Databases (e.g., Pinecone, Milvus, Weaviate) and Feature Stores (e.g., Feast, Tecton, Hopsworks).
Optimize data retrieval patterns to support real-time AI applications and agentic workflows.
Oversee Model Hosting environments, utilizing Kubernetes (K8s) and GPU orchestration to manage compute resources efficiently.
Enable Developer Self-Service
Build and maintain a Self-Service Portal or CLI that allows product squads to provision AI environments, models, and data stores independently.
Reduce "Time-to-Inference" for new features by providing pre-configured templates and blueprints.
Conduct internal workshops and provide documentation to empower squads to use the platform effectively.

Requirements

What you’ll need

Must-Have Technical Skills
Infrastructure: Deep experience with Kubernetes (K8s), Docker, and Terraform/Pulumi.
Hybrid Cloud: Proven experience managing workloads across AWS/Azure/GCP and On-Premises (NVIDIA AI Enterprise, OpenShift).
AI/ML Tooling: Hands-on experience with vLLM, TGI (Text Generation Inference), or NVIDIA Triton for model serving.
Databases: Expertise in Vector DBs and traditional SQL/NoSQL databases.
Languages: High proficiency in Python and Go or Rust for platform tooling.
Experience 8+ years in Platform Engineering, DevOps, or Site Reliability Engineering (SRE).
2+ years specifically focused on building AI/ML infrastructure or platforms.
Experience building Internal Developer Platforms (IDP) is a massive plus.

Benefits

Comp & perks

Professional development opportunities