AI Platform Engineer
hace 5 días
Role
: AI Platform Engineer (
Data, Infrastructure & Agentic Systems
)
Location
: Barcelona, Spain
6 months extendable contract
Mandatory:
agentic AI
Key Responsibilities
- Engineer robust data and cloud foundations
to support AI workloads and automation at scale across AWS environments. - Design and implement agentic AI systems
as part of a broader automation stack—developing self-orchestrating workflows, APIs, and reusable platform components. - Build automation and observability solutions
using Infrastructure-as-Code (Terraform/CDK) and modern monitoring frameworks. - Integrate data pipelines
with intelligent orchestration layers to enable autonomous experimentation and deployment. - Collaborate across disciplines
—partnering with data engineers, AI scientists, and platform specialists to embed AI capabilities into core operations. - Champion responsible and scalable AI automation
, ensuring compliance, reliability, and transparency by design.
Required Experience & Skills
- Strong software engineering background with
Python, TypeScript
, or similar languages. - Proven experience building and automating cloud infrastructure using
AWS
(EC2, IAM, S3, Lambda, CloudWatch, etc.) and
Infrastructure-as-Code
tools (Terraform/CDK). - Solid understanding of
data engineering principles
—pipeline design, data flow orchestration, and API integration. - Experience with
CI/CD pipelines
and Git-based workflows. - Demonstrated experience in use of
AI tools
(e.g., claude, Copilot, LangChain, agent frameworks) to enhance productivity. A
self-driven, curious problem solver
who experiments and iterates toward impactful solutions.Experience deploying or integrating
agentic AI architectures
, such as orchestration frameworks or autonomous agents.- Knowledge of
RAG pipelines
, embeddings, and vector databases (e.g., Weaviate, Qdrant). - Familiarity with
Kubernetes/EKS
and observability tools (New Relic, OpenTelemetry). - Understanding of
model hosting and optimization
(vLLM, Triton, HuggingFace). - Awareness of
data governance and responsible AI practices
in enterprise or regulated settings.
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