AI Platform & MLOps Engineering
AI Platform & MLOps Engineering is an individual-contributor Data + AI career progressing from guided delivery to enterprise expertise without requiring people management.
- Función
- Data & Analytics
- Arquetipo
- Engineering
- Nivel más alto
- L7 · Distinguished AI Platform Engineer
Para qué existe
Builds and operates platforms, deployment systems, evaluations, observability and controls required to run ML and AI reliably at production scale.
Responsabilidades típicas
Deliver ai platform & mlops engineering outcomes; apply data/AI standards; manage quality, reliability and risk; collaborate across product, engineering, security, legal and business teams; at senior levels shape enterprise standards and strategy.
Dónde se trabaja
Technology companies, data/cloud platforms, financial services, consulting, shared services, product engineering and regulated enterprises adopting Data + AI.
Cómo avanza la carrera
Associate AI Platform Engineer → AI Platform Engineer → Senior AI Platform Engineer → Staff AI Platform Engineer → Senior Staff AI Platform Engineer → Principal AI Platform Engineer → Distinguished AI Platform Engineer
Niveles de esta carrera
Seis etapas estándar. La séptima existe solo donde la carrera la contempla.
- L1Associate AI Platform Engineer
- L2AI Platform Engineer
- L3Senior AI Platform Engineer
- L4Staff AI Platform Engineer
- L5Senior Staff AI Platform Engineer
- L6Principal AI Platform Engineer
- L7Distinguished AI Platform Engineer
Nivel distinguido opcional. No todas las carreras llegan acá.
Qué se espera en L2 · AI Platform Engineer
Apply ai platform & mlops engineering capability at independently delivers standard work within a team or defined domain.
Las cinco dimensiones que cambian
- Autonomía
- Independent routine ownership
- Alcance
- Independently delivers standard work within a team or defined domain.
- Complejidad
- Moderate
- Influencia
- Immediate team
- Impacto en el negocio
- Task/team
- Ambigüedad
- Moderate
Cómo se ve un buen desempeño
Produces trustworthy ai platform & mlops engineering outcomes, makes sound trade-offs, communicates evidence and risks, and demonstrates the autonomy and impact expected at L2.
Evidencia típica
Completed ai platform & mlops engineering work products or production outcomes; documented decisions; measurable improvements in quality, reliability, risk, speed, cost or business outcomes; peer/stakeholder evidence of L2 scope.
Habilidades esperadas en L2
Agrupadas en humanas, profesionales y técnicas. La proficiencia meta usa la escala P1–P7, y cada fila dice qué significa ese nivel para esa habilidad.
Humanas8
| Humanas | P | De apoyo |
|---|---|---|
| Clear verbal communicationCommunication & Language | P2FoundationalDe apoyo | Applies Clear verbal communication to routine situations using established methods and controls. |
| Clear written communicationCommunication & Language | P2FoundationalDe apoyo | Applies Clear written communication to routine situations using established methods and controls. |
| CollaborationCollaboration & Relationships | P2FoundationalDe apoyo | Applies Collaboration to routine situations using established methods and controls. |
| Critical thinkingThinking & Problem Solving | P2FoundationalDe apoyo | Applies Critical thinking to routine situations using established methods and controls. |
| Cross-functional collaborationCollaboration & Relationships | P2FoundationalDe apoyo | Applies Cross-functional collaboration to routine situations using established methods and controls. |
| Learning agilityExecution & Self-Management | P2FoundationalDe apoyo | Applies Learning agility to routine situations using established methods and controls. |
| Stakeholder alignmentInfluence & Leadership Without Authority | P2FoundationalDe apoyo | Applies Stakeholder alignment to routine situations using established methods and controls. |
| Structured problem solvingThinking & Problem Solving | P2FoundationalDe apoyo | Applies Structured problem solving to routine situations using established methods and controls. |
Profesionales6
| Profesionales | P | De apoyo |
|---|---|---|
| AI lifecycle governanceData & Analytics Practice | P2FoundationalCentral | Applies AI lifecycle governance to routine situations using established methods and controls. |
| AI model governanceData & Analytics Practice | P2FoundationalCentral | Applies AI model governance to routine situations using established methods and controls. |
| Architecture governanceIT Service Management & Governance | P2FoundationalDe apoyo | Applies Architecture governance to routine situations using established methods and controls. |
| Model risk managementData & Analytics Practice | P2FoundationalCentral | Applies Model risk management to routine situations using established methods and controls. |
| Responsible AI principlesData & Analytics Practice | P2FoundationalDe apoyo | Applies Responsible AI principles to routine situations using established methods and controls. |
| Technology governanceIT Service Management & Governance | P2FoundationalDe apoyo | Applies Technology governance to routine situations using established methods and controls. |
Técnicas10
| Técnicas | P | De apoyo |
|---|---|---|
| AI observabilityData Science, ML & AI | P2FoundationalDe apoyo | Uses AI observability for routine tasks with documented patterns and review. |
| Feature pipeline engineeringData Science, ML & AI | P2FoundationalDe apoyo | Uses Feature pipeline engineering for routine tasks with documented patterns and review. |
| Feature store engineeringData Science, ML & AI | P2FoundationalDe apoyo | Uses Feature store engineering for routine tasks with documented patterns and review. |
| ML experiment trackingData Science, ML & AI | P2FoundationalDe apoyo | Uses ML experiment tracking for routine tasks with documented patterns and review. |
| MLOpsData Science, ML & AI | P2FoundationalCentral | Uses MLOps for routine tasks with documented patterns and review. |
| MLflowData Science, ML & AI | P2FoundationalCentral | Uses MLflow for routine tasks with documented patterns and review. |
| Model deploymentData Science, ML & AI | P2FoundationalCentral | Uses Model deployment for routine tasks with documented patterns and review. |
| Model monitoringData Science, ML & AI | P2FoundationalCentral | Uses Model monitoring for routine tasks with documented patterns and review. |
| Model registryData Science, ML & AI | P2FoundationalDe apoyo | Uses Model registry for routine tasks with documented patterns and review. |
| Model servingData Science, ML & AI | P2FoundationalCentral | Uses Model serving for routine tasks with documented patterns and review. |
Qué cambia al pasar de L2 a L3
Moving from AI Platform Engineer to Senior AI Platform Engineer means greater autonomy, complexity, scope, influence and evidence of impact—not simply more tools.
Cómo se demuestra que ya estás
Sustained evidence of operating at L3-type scope: handles representative complexity, influences expected stakeholders and produces durable measurable outcomes.
Cómo prepararse
Take on one assignment at next-level scope; seek feedback; document decisions/outcomes; mentor others where appropriate; close highest-priority skill gaps.
Carreras cercanas
Calculadas por habilidades compartidas. Es una señal para explorar, no una garantía de contratación ni de elegibilidad.
- AI Governance & Responsible AIData & Analytics14 habilidades compartidas
- Data ArchitectureData & Analytics14 habilidades compartidas
- Analytics EngineeringData & Analytics12 habilidades compartidas
- Applied AI / Applied ScienceData & Analytics12 habilidades compartidas
- Data Governance & StewardshipData & Analytics9 habilidades compartidas
- Machine Learning EngineeringData & Analytics9 habilidades compartidas
Marco de carreras v21, activo desde 17 de agosto de 2026.