Lead AI Application Engineer – Infrastructure, LLMOps

🕒 June 19

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TechBiz Global

WebsiteLinkedInAll Job Openings

51 - 200 employees

📣 Marketing

📦 Logistics

🎯 Recruiter

Marketing • Logistics • Recruitment

TechBiz Global is a leading recruitment and software development company that specializes in connecting businesses with top-tier talent across 28+ countries. They serve clients from startups to major enterprises, providing expertise in hiring key roles in industries such as FinTech. In addition to talent acquisition, TechBiz Global offers comprehensive software development services to turn clients' visions into reality, powering digital transformations with expert engineers. The company also provides strategic CTO consulting services and flexible outstaffing and outsourcing solutions, helping businesses manage growth and optimize technology processes. Recognized as a top IT recruitment and consulting firm, TechBiz Global is dedicated to delivering personalized and innovative solutions to its clients, ensuring they have the tools necessary for success.

📋 Description

• 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

• 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

• Professional development opportunities