MLOps Engineer
Znane również jako: ML Platform Engineer
Builds and maintains the infrastructure with which machine learning models are trained, deployed and monitored, and automates retraining when models grow stale.
The mlops engineer ensures data scientists' models go into production reliably and repeatably, with automated pipelines for training, validation and rollout. Unlike the machine learning engineer who focuses on a specific model, they build the platform on which multiple models run at once. Selection often focuses on knowledge of ml models, while recognising that a pipeline silently uses stale data instead of training fresh is the biggest source of unnoticed quality decay in practice.
Kompetencje, które należy oceniać
Oto konstrukty behawioralne i kompetencyjne zawarte w tym profilu. Przy każdym z nich podano, który kwestionariusz hrmforce służy do jego pomiaru.
| Kompetencje, które należy oceniać | Poziom docelowy | hrmforce narzędzie | Metoda oceny | Kotwica behawioralna na poziomie docelowym |
|---|---|---|---|---|
| Numerical reasoning Knockout | N5 | Ability Scan | Cognitive ability test | Sees in complex number series which assumptions determine the outcome and sets the calculation logic that others in the organisation follow. |
| Curiosity and exploring Kompetencje (50-ramowe): Learning Ability | N4 | Big Fifty, 15PF, Motivation | Personality questionnaire | Gathers information on new methods outside the own team and brings findings into the work meeting unprompted. |
| Dealing with ambiguity Kompetencje (50-ramowe): Flexibility | N4 | Big Fifty, Mental Resilience (4C), Competency Check | Situational Judgement Test | Formulates explicit assumptions when information is missing, proceeds on that basis and revises the approach once new data arrives. |
Krótki profil
Dziesięć najważniejszych umiejętności w tym profilu. Pełny profil zawierający wszystkie umiejętności, punkty odniesienia behawioralne i metody oceny znajduje się w pliku do pobrania.
| T | Umiejętności | Poziom docelowy | Waga | Knockout | Metoda oceny |
|---|---|---|---|---|---|
| A | Numerical reasoning | N5 | 5 | tak | Cognitive ability test |
| V | Data analysis | N5 | 5 | tak | Work sample test |
| K | Basic statistical interpretation | N5 | 5 | tak | Knowledge test |
| K | Machine learning fundamentals | N4 | 5 | tak | Knowledge test |
| V | Critical thinking and source evaluation | N5 | 4 | nie | Work sample test |
| V | Visual communication and data storytelling | N5 | 4 | nie | Work sample test |
| V | Data visualisation | N5 | 4 | nie | Work sample test |
| V | Assessing data quality | N5 | 4 | tak | Work sample test |
| V | Reporting and dashboard building | N5 | 4 | nie | Work sample test |
| V | Data modelling | N5 | 4 | nie | Work sample test |
Zobacz wszystkie 25 umiejętności w pełnym profilu
Narzędzia oceny dla tego stanowiska
Work sample test
Representative work sample under standardized conditions
Knowledge test (client-specific)
Professional knowledge test, customized for each client
Competency Check
Behavioral-Level Competency Assessment
Portfolio
Evaluation of prior work against fixed criteria
Ability Scan
Aptitude Test: Verbal, Numerical, and Abstract Reasoning Skills
Structured interview
Behavioral interview with a fixed structure
Big Fifty
Personality questionnaire, 150 items, 25 factors
15PF
Personality Profile, a shorter alternative to the Big Fifty
Motivation
Drivers and sources of motivation
360 Feedback
Multiple assessments based on behavioral anchors
Mental Resilience (4C)
Mental Resilience According to the 4C Model
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