2 publicaciones de empleo destacadas de cloud architect / multi‑cloud en barcelona, ct (¡Están contratando!)

Lead AI Application Engineer (Infrastructure & LLMOps)
TechBiz Global GmbH —Barcelona, Barcelona provincia
- Jornada completa
- Teletrabajo
- Azure
- DevOps
- SQL
Solicitud rápida
Senior Backend Engineer (Go) | Studio AI
Creative Fabrica —Barcelona, Barcelona provincia
- AWS
- APIs
- Inteligencia artificial

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Lead AI Application Engineer (Infrastructure & LLMOps)
TechBiz Global GmbH —Barcelona, Barcelona provincia
Standardize how squads interact with LLMs, providing unified APIs and abstraction layers to prevent vendor lock-in. Build & Run the Shared AI Platform.
Solicitud rápida
Senior Backend Engineer (Go) | Studio AI
Creative Fabrica —Barcelona, Barcelona provincia
Data & Infrastructure: Work with AWS to manage massive data throughput and integrate with high-performance storage and compute clusters.

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Lead AI Application Engineer (Infrastructure & LLMOps)

TechBiz Global GmbH
Barcelona, Barcelona provincia

Solicitud rápida

Detalles del empleo

Jornada completa

Cualificaciones

Azure
DevOps
NoSQL
SQL
AWS
Docker
Grado
Terraform
APIs
Cloud
Inteligencia artificial
Python

Descripción completa del empleo

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.

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Lead AI Application Engineer (Infrastructure & LLMOps)

Senior Backend Engineer (Go) | Studio AI

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