Lead AI Application Engineer
Pubblicato il 26-10-2026 - TechBiz Global in Roma
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:
1. 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.