- Machine Learning e MLOps (Model Engineering, Deployment, Monitoring) e campi associati (ad esempio DataOps, Data Engineering, Model Engineering)
- Componenti di AIOps, data fabric, data lake o data mesh, tra cui anche strumenti di ETL e message passing
- Esperienza con data science and machine learning engineering platforms quali Valohai, A naconda Enterprise, Databricks MLReef, ZenML, HuggingFace, Wandb, MLFlow, MetaFlow, TensorFlow Extended (TFX), Cloudera, ProActive AI Orchestration, DataRobot, Dataiku, Domino, Iguazio, Verta,
- Esperienza con le soluzioni di Data Analytics, MLOps e Data Platform di uno o più dei principali Cloud provider (e.g. Amazon SageMaker, Microsoft Azure Machine Learning, Google Vertex AI, IBM Cloud Pak for Data)
- Esperienza nella collaborazione con team di Data Scientist e supporto degli stessi nella definizione ed implementazione di data science and machine learning engineering platforms
- Esperienza nell'implementazione di librerie e/o SDK in Python.
- Conoscenze delle metodologie di Continuous Delivery e Continuous Integration
- Competenze con uno o più degli strumenti di CI/CD (ad esempio Jenkins, GitHub Actions, GitLab CI), anche in ambito Cloud Native Continuous Delivery (ad esempio Keptn, Flux, Argo, Tekton)
- Esperienza dei principali tool di infrastructure-as-code (ad esempio Terraform, Ansible), e metodologia GitOps
- Esperienza in strumenti di containerizzazione (ad esempio Docker, podman, buildah, Cloud Native Buildpacks)
- Esperienza come amministratore o sviluppatore di applicazioni e/o modelli di machine learning in ambienti basati su Kubernetes (ad esempio Kubernetes, K3s, Rancher), preferibilimente con certificazioni CKA
- Competenze sistemistiche Linux
- Esperienza come technical project lead in un contesto accademico o di consulenza, o all'interno di un reparto di progettazione
- Conoscenza di standard di Service Management o Project Management, o certificazioni associate (ad esempio PRINCE2, ITIL, FitSM, AgilePM)
- Buona conoscenza della lingua inglese dal punto di vista professionale
- Utilizzo di strumenti di AI per l'efficientamento delle pratiche di software engineering e/o per l'integrazione di AI nel sotware
- Red Hat OpenShift AI
- Apache Spark, Apache Flink, Apache Hadoop, Neo4j, OpenAI
- Stack Elastic (Kibana, Logstash, Beats), od in alternativa stack Grafana (Grafana, Loki, Prometheus, Promtail)
- Approcci DevSecOps:
- Strumenti di code quality (ad esempio SonarSource, Mend)
- Software supply chain management (ad esempio Snyk, Trivy) e metodi associati (SAST, DAST)
- Premio fedeltà (se rimani con noi 36 mesi)
- Formazione continua: 1 giorno al mese dedicato alla formazione tramite l'ausilio di piattaforme dedicate
- Working from anywhere (1 mese all'anno o 4 settimane)
- Recharging Friday (1 venerdì a trimestre retribuito)
- Voce amica
- Ticket Restaurant elettronici
- Welfare aziendale
- Lavoro in un contesto aziendale giovane, dinamico, tecnologicamente innovativo
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Cloud Native Artificial Intelligence Engineer - Italia - Kiratech S.p.A.
2 giorni fa
Descrizione
Dal 2005, in Kiratech ci occupiamo di fornire consulenza, formazione e servizi in ambito Cloud Native e di aiutare le aziende ad adottare l'approccio Platform in modo efficace; siamo infatti membri della Cloud Native Computing Foundation (CNCF), partner dei principali Cloud Provider ed accogliamo al nostro interno un team con expertise in platform engineering, infrastructure engineering, Cloud computing, Cloud Native cybersecurity, site reliability engineering, software engineering, machine learning engineering, e data engineering.
Il nostro obiettivo è ascoltare le richieste dei clienti per guidarli verso una maggiore efficienza e produttività, facendo dell'IT un acceleratore del business.
Kiratech fa per te se cerchi un'azienda in cui:
· l'attenzione alla persona è fondamentale
· lavorare fianco a fianco con esperti del settore
· sviluppare e consolidare competenze pratiche e concrete
· collaborare all'interno di un team coeso
· lavorare per obiettivi
Kiratech è alla ricerca di una/un Cloud Native Artificial Intelligence Engineer per il suo Engineering Team.
La figura si occuperà di contribuire attivamente come parte di un team, su progetti attivi presso i clienti di Kiratech nell'adozione di tecnologie e pratiche di data science and machine learning engineering platforms e Cloud Native.
La persona che meglio si presta a questo ruolo ha esperienza accademica o lavorativa nella definizione e/o implementazione di data science and machine learning engineering platforms, ha conoscenza di tecnologie Cloud Native, ha un'attitudine creativa e propositiva davanti a complessità socio-tecniche ed è in grado di analizzare problematiche tecniche, proporre soluzioni e comunicarle in modo convincente a stakeholders sia a livello tecnico sia al livello di business.
Conoscenze e competenze necessarie:
Tecniche
Technical Project Lead
Lingue
Costituiscono titolo preferenziale certificazione o esperienza dimostrabile in:
Cosa offriamo?
