Platform Engineer – AI Enablement & Automation (w/m/d)

Stellenbeschreibung:

Overview

We’re looking for an AI Platform Engineer to build the foundation for an agentic AI platform that connects data, models, and agents and runs reliably at cloud scale. This is a hands‑on engineering role at the intersection of data, infrastructure, and AI—you’ll ship usable platform components, not just infrastructure, and actively use AI tools to accelerate your own work. The goal: safe, observable, and scalable AI automation that teams can adopt quickly.

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—develop 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.

Must have 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.

Nice to have Skills

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.