Summary: Seeking a dedicated Lead AI Application Engineer to build and run a shared AI platform, curate AI services, and manage AI data infrastructure for a client's innovative team. Highlights: 1. Architect and maintain a multi-tenant AI Platform for full ML lifecycle. 2. Develop and expose "as-a-service" AI capabilities. 3. Enable developer self-service with AI environments and data stores. At TechBiz Global, we are providing recruitment service to our TOP clients from our portfolio. We are currently looking for a dedicated **Lead AI Aplication Engineer** to join one of our **clients' teams**. If you're looking for an exciting opportunity to grow in an innovative environment, this could be the perfect fit for you. **Key Responsibilities:** * **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. **2\. 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. **3\. 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. **4\. 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. **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.
David Muñoz
Indeed · HR
David Muñoz
Indeed · HR
