Senior MLOps Engineer

RAIVA Technologies UG (haftungsbeschränkt); Potsdam; Vor Ort; EUR 70.000 - 90.000; Vollzeit

Zusammenfassung

RAIVA is looking for an experienced professional in Potsdam to manage and optimize our ML models and embedding pipelines. The ideal candidate will have over 5 years of experience in MLOps and be proficient with vector databases and Python.

You will be responsible for automating model rollouts and ensuring high-quality evaluations for retrieval systems. Join us to help enhance our AI capabilities in a fast-paced environment.

Qualifikationen

• 5+ years in MLOps / ML Platform / Applied ML Infrastructure.
• Experience with vector databases and embedding pipelines.
• Strong skills in Python and job orchestration.

Aufgaben

• Own the embedding pipeline including ingestion and indexing.
• Build evaluation harnesses and quality dashboards.
• Automate model rollouts and track quality metrics.

Kenntnisse

MLOps

Vector databases (Qdrant preferred)

Python

Job orchestration

Evaluation design (offline and online)

Tools

Qdrant

Docker

MLflow

Airflow

Jobbeschreibung

Industrialize our retrieval and reasoning models — from embedding pipelines to evals to safe rollouts.

About the role

You sit between AI Engineering and Platform. You make sure models, embeddings, and pipelines ship safely, are evaluated continuously, and can be rolled back without drama.

What you'll do

• Own the embedding pipeline: ingestion, chunking, multi-embedding, reindexing
• Build eval harnesses and quality dashboards for retrieval and answer quality
• Operate Qdrant collections, index versioning, and zero-downtime reindexing
• Automate model/version rollouts with shadow traffic and canary patterns
• Track drift, latency, cost, and quality per domain (legal, financial, real estate)

What we look for

• 5+ years in MLOps / ML Platform / Applied ML Infrastructure
• Hands-on with vector databases (Qdrant preferred), embedding pipelines, and reranking
• Strong Python; solid with batch + streaming job orchestration
• Experience designing offline and online evals for retrieval/LLM systems
• Comfortable owning data + model lifecycle end-to-end

Nice to have

• Experience with Cohere / OpenAI / Gemini production usage at scale
• Background with feature stores, experiment tracking (MLflow, W&B)
• Prior work on RAG quality monitoring or hallucination detection

Stack & tools

Python

Supabase

Qdrant

MLflow

Airflow

Docker

OpenTelemetry

How to apply

Send a short note explaining why you're a fit, plus 1–3 concrete artifacts (code, writing, deals closed, products shipped). We read every thoughtful application.