What are the responsibilities and job description for the Lead AI Application Engineer (Infrastructure & LLMOps) position at TechBiz Global GmbH?
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.