Senior Technical Data & AI Consultant*
inovex GmbH
Source: Himalayas ↗
Working arrangement is supplied by the provider. Check the original advert for attendance and travel requirements.
Provider location / applicant eligibility: Worldwide
About the role
Skills mentioned in the listing
Recognised skills are extracted from the supplied description using canonical names and aliases. They are mentions, not verified essential requirements. Check the source advert for the employer’s full requirements and location restrictions.
Why these skills appear
Python · mentioned
ery, Azure Data Platform & Fabric, AWS Data & ML Stack, GCP Data Engineering: Python, SQL, Spark, Kafka, Airflow, dbt, relationale und NoSQL-Datenbanken ML & AI: mathematische Optimierung, klassisches Machine Learnin
SQL · mentioned
re Data Platform & Fabric, AWS Data & ML Stack, GCP Data Engineering: Python, SQL, Spark, Kafka, Airflow, dbt, relationale und NoSQL-Datenbanken ML & AI: mathematische Optimierung, klassisches Machine Learning, MLOps
Flask · mentioned
, LLMs, LLMOps, Agentic AI, RAG-Architekturen Deployment & Delivery: FastAPI, Flask, Docker, Kubernetes, CI/CD, Cloud-native Architekturen Requirements Wer gut zu uns passen würde Beratungspersönlichkeit: Du bringst
Docker · mentioned
LLMOps, Agentic AI, RAG-Architekturen Deployment & Delivery: FastAPI, Flask, Docker, Kubernetes, CI/CD, Cloud-native Architekturen Requirements Wer gut zu uns passen würde Beratungspersönlichkeit: Du bringst mindes
Kubernetes · mentioned
Agentic AI, RAG-Architekturen Deployment & Delivery: FastAPI, Flask, Docker, Kubernetes, CI/CD, Cloud-native Architekturen Requirements Wer gut zu uns passen würde Beratungspersönlichkeit: Du bringst mindestens 5 J
AWS · mentioned
KI-Plattformen: Databricks, Snowflake, BigQuery, Azure Data Platform & Fabric, AWS Data & ML Stack, GCP Data Engineering: Python, SQL, Spark, Kafka, Airflow, dbt, relationale und NoSQL-Datenbanken ML & AI: mathemati
Azure · mentioned
dir regelmäßig: Daten- & KI-Plattformen: Databricks, Snowflake, BigQuery, Azure Data Platform & Fabric, AWS Data & ML Stack, GCP Data Engineering: Python, SQL, Spark, Kafka, Airflow, dbt, relationale und NoSQL-Date
Google Cloud · mentioned
bricks, Snowflake, BigQuery, Azure Data Platform & Fabric, AWS Data & ML Stack, GCP Data Engineering: Python, SQL, Spark, Kafka, Airflow, dbt, relationale und NoSQL-Datenbanken ML & AI: mathematische Optimierung, kla
CI/CD · mentioned
RAG-Architekturen Deployment & Delivery: FastAPI, Flask, Docker, Kubernetes, CI/CD, Cloud-native Architekturen Requirements Wer gut zu uns passen würde Beratungspersönlichkeit: Du bringst mindestens 5 Jahre fundier
Machine learning · mentioned
tionale und NoSQL-Datenbanken ML & AI: mathematische Optimierung, klassisches Machine Learning, MLOps, Generative AI, LLMs, LLMOps, Agentic AI, RAG-Architekturen Deployment & Delivery: FastAPI, Flask, Docker, Kuberne
Spark · mentioned
ta Platform & Fabric, AWS Data & ML Stack, GCP Data Engineering: Python, SQL, Spark, Kafka, Airflow, dbt, relationale und NoSQL-Datenbanken ML & AI: mathematische Optimierung, klassisches Machine Learning, MLOps, Gen
Databricks · mentioned
st. In unseren Projekten begegnen dir regelmäßig: Daten- & KI-Plattformen: Databricks, Snowflake, BigQuery, Azure Data Platform & Fabric, AWS Data & ML Stack, GCP Data Engineering: Python, SQL, Spark, Kafka, Airflow
Snowflake · mentioned
en Projekten begegnen dir regelmäßig: Daten- & KI-Plattformen: Databricks, Snowflake, BigQuery, Azure Data Platform & Fabric, AWS Data & ML Stack, GCP Data Engineering: Python, SQL, Spark, Kafka, Airflow, dbt, relat
Data engineering · mentioned
, Snowflake, BigQuery, Azure Data Platform & Fabric, AWS Data & ML Stack, GCP Data Engineering: Python, SQL, Spark, Kafka, Airflow, dbt, relationale und NoSQL-Datenbanken ML & AI: mathematische Optimierung, klassisch