Über diese AI Engineer Stelle bei Vacancies
In this role, you will
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Design and build RAG and automation workflows inside our VPC environment.
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Create integrations across engineering, communication, and knowledge-management systems (Jira, Confluence, Slack, Google Drive, Git).
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Develop AI-powered pipelines for code quality checks, ticket auto-classification, documentation updates, release notes, and meeting summaries.
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Work on prompt engineering, model selection and routing (Haiku / Sonnet / Opus), and workflow optimization.
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Build evaluation and regression frameworks for AI workflows to prevent model-upgrade regressions.
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Create lightweight dashboards for engineering leads to surface delivery patterns and bottlenecks.
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Own initiatives end-to-end: scope → design → ship → measure → iterate.
It’s all about you
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5+ years of production experience with Python (services, async, API integrations, clean and testable code).
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Hands-on experience with LLM APIs in production: Anthropic Claude (Opus/Sonnet/Haiku), OpenAI, AWS Bedrock.
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Strong understanding of RAG systems: chunking, embeddings, hybrid search, re-ranking, grounding/citations.
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Prompt engineering + structured outputs + tool/function calling.
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Experience with eval/regression frameworks: Promptfoo, Ragas, LangSmith, or custom evaluation harnesses.
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Agentic patterns: ReAct, function-calling loops, fallbacks.
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Vector databases: pgvector, Pinecone.
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Workflow orchestration: n8n, Airflow, or custom orchestration systems.
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Observability: token-spend tracing, latency monitoring, model routing.
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Solid AWS fundamentals: IAM, Lambda, S3, CloudTrail — comfortable deploying inside VPC environments independently.
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CI/CD + pipeline hooks (GitLab/GitHub), webhooks, event-driven architectures.
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Basic SQL skills for analytical queries and metrics dashboards.
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Driver mindset: proactively identifies the highest-leverage opportunity and drives it to production
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B2 English level
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Real REST integrations with 2–3+ APIs
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Experience with workflow orchestration tools.
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Prompt evaluation tooling such as LangSmith is nice to have
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Experience with Docker
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RAG experience: embeddings and vector stores (pgvector, Pinecone, or similar) for knowledge base use cases
Would be a plus
What we offer
Hiring process
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Intro call
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Technical Interview
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Final Interview
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Reference check
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Offer
