Noch keine KI-Analyse
Ausführen: python script_run_ai_scoring.py --profile alex
Geforderte Skills
ai agents
ai governance
api
azure
ci/cd
classification
cloud computing
communication
data formats
databricks
devops
eu ai act
generative ai
git
javascript
kpi
large language models
machine learning
mcp
mlflow
monitoring
n8n
problem solving
process automation
prompt engineering
python
python web frameworks
rag
relational databases
sap
testing
transformers
ux/ui design
vector databases
Stellenbeschreibung
Duration: 12 months+ | Start: 11/2026 | Remote: 100%
Beschreibung
IT
Multi-Agent Systems
Prompt Engineering
Generative AI
Description
Senior Agentic System Engineer (m/w/d)
Start: asap
Duration: Longterm (1year +)
Location: 100% Remote (CET timezone)
Language: English
Important:
Within Germany, only candidates who are in employment subject to social security contributions can be considered. No freelancers.
Freelancers based outside Germany are welcome.
Must-Have-Skills:
Agentic AI systems
LangGraph - design compiled multi-agent graphs: nodes, conditional edges, typed state schemas with correct reducer functions, intent routing, deterministic workflow spines
Human-in-the-loop design - interrupt and resume with HANA-persisted checkpoints; keep the human in control of consequential actions
Understanding Transformer Architecture - Attention mechanisms and enforced Tool-Calling
A2A protocol - implement agent servers: agent card, skill advertisement, streaming JSON-RPC over SSE
MCP - design and host tool surfaces (e.g. via @cap-js/mcp)
Prompt engineering - output format constraints, PII leakage prevention, domain scoping; test under adversarial input, not just happy paths
Know where LLM autonomy ends and deterministic code must take over - and have learned this from production failures, not tutorials
Apply a cost/benefit test before building an agent - a deterministic pipeline with one LLM call is often better
Semantic Search for context ingestion
Platform
MLflow - instrument agent runs, log metrics and token usage per turn, compare model and prompt versions across releases
Vector database experience - RAG pipelines and semantic search (HANA Cloud Vector Engine or similar)
Python
Async Python 3.13 - async/await, ContextVars, asyncio.to_thread for blocking drivers; know what stalls an event loop and how to fix it
pytest + pytest-asyncio - test suites including async patterns; coverage discipline
python backend development with e.g. starlet or fastapi
General
Architecture documentation - write and maintain ADRs; keep them current as systems evolve
Cross-team alignment - coordinate API contracts, data schemas, and event formats with consuming teams before shipping
Operational ownership - monitor, triage, fix; own the health of what you build
Azure DevOps / GitHub Actions - CI/CD pipeline maintenance and troubleshooting
Fluent English; experience in distributed international teams
Good to have ...
AI observability and experiment tracking
Build evaluation datasets; run LLM-as-judge or golden-dataset assertion checks
OpenTelemetry - distributed traces across agent turns and downstream services; OTLP export to SAP BTP Cloud Logging
Dynatrace / Kibana - triage latency spikes and errors by agent node
Token usage tracking per model - cost-control signal, not a curiosity
Platform
Cloud Foundry - MTA deployment, service binding, VCAP_SERVICES credential resolution
Databricks (e.g. for MlFlow hosting)
SAP AI Core, Launchpad, SDK, Generative AI Hub
Identity propagation - carry caller identity through agent and tool layers so authorization checks run under the human's principal, not the service account
Nice to have ...
ruff + mypy + pre-commit - linting, type checking, hook enforcement
N8N - low-code workflow automation for rapid agentic prototyping
EU AI Act and AI ethics - risk classification, transparency requirements, responsible AI patterns for enterprise agents