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.
- Function
- Data & Analytics
- Archetype
- Engineering
- Highest level
- L7 · Distinguished AI Platform Engineer
Why it exists
Builds and operates platforms, deployment systems, evaluations, observability and controls required to run ML and AI reliably at production scale.
Typical responsibilities
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.
Where the work happens
Technology companies, data/cloud platforms, financial services, consulting, shared services, product engineering and regulated enterprises adopting Data + AI.
How the career progresses
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
Levels in this career
Six standard stages. The seventh exists only where the career provides for it.
- 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
Optional distinguished level. Not every career reaches it.
What is expected at L1 · Associate AI Platform Engineer
Apply ai platform & mlops engineering capability at learns the discipline and executes defined work with guidance.
The five dimensions that change
- Autonomy
- Close guidance
- Scope
- Learns the discipline and executes defined work with guidance.
- Complexity
- Defined
- Influence
- Immediate team
- Business impact
- Task/team
- Ambiguity
- Low
What good looks like
Produces trustworthy ai platform & mlops engineering outcomes, makes sound trade-offs, communicates evidence and risks, and demonstrates the autonomy and impact expected at L1.
Typical evidence
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 L1 scope.
Skills expected at L1
Grouped as human, professional and technical. Target proficiency uses the P1–P7 scale, and each row says what that level means for that particular skill.
Human6
| Human | P | Supporting |
|---|---|---|
| Clear verbal communicationCommunication & Language | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Clear verbal communication; works with close guidance. |
| Clear written communicationCommunication & Language | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Clear written communication; works with close guidance. |
| CollaborationCollaboration & Relationships | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Collaboration; works with close guidance. |
| Critical thinkingThinking & Problem Solving | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Critical thinking; works with close guidance. |
| Cross-functional collaborationCollaboration & Relationships | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Cross-functional collaboration; works with close guidance. |
| Structured problem solvingThinking & Problem Solving | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Structured problem solving; works with close guidance. |
Professional6
| Professional | P | Supporting |
|---|---|---|
| AI lifecycle governanceData & Analytics Practice | P1AwarenessCore | Understands the purpose, vocabulary and basic principles of AI lifecycle governance; works with close guidance. |
| AI model governanceData & Analytics Practice | P1AwarenessCore | Understands the purpose, vocabulary and basic principles of AI model governance; works with close guidance. |
| Architecture governanceIT Service Management & Governance | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Architecture governance; works with close guidance. |
| Model risk managementData & Analytics Practice | P1AwarenessCore | Understands the purpose, vocabulary and basic principles of Model risk management; works with close guidance. |
| Responsible AI principlesData & Analytics Practice | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Responsible AI principles; works with close guidance. |
| Technology governanceIT Service Management & Governance | P1AwarenessSupporting | Understands the purpose, vocabulary and basic principles of Technology governance; works with close guidance. |
Technical8
| Technical | P | Supporting |
|---|---|---|
| AI observabilityData Science, ML & AI | P1AwarenessSupporting | Recognizes the purpose, core concepts and risks of AI observability; performs only guided exercises. |
| ML experiment trackingData Science, ML & AI | P1AwarenessSupporting | Recognizes the purpose, core concepts and risks of ML experiment tracking; performs only guided exercises. |
| MLOpsData Science, ML & AI | P1AwarenessCore | Recognizes the purpose, core concepts and risks of MLOps; performs only guided exercises. |
| MLflowData Science, ML & AI | P1AwarenessCore | Recognizes the purpose, core concepts and risks of MLflow; performs only guided exercises. |
| Model deploymentData Science, ML & AI | P1AwarenessCore | Recognizes the purpose, core concepts and risks of Model deployment; performs only guided exercises. |
| Model monitoringData Science, ML & AI | P1AwarenessSupporting | Recognizes the purpose, core concepts and risks of Model monitoring; performs only guided exercises. |
| Model registryData Science, ML & AI | P1AwarenessSupporting | Recognizes the purpose, core concepts and risks of Model registry; performs only guided exercises. |
| Model servingData Science, ML & AI | P1AwarenessCore | Recognizes the purpose, core concepts and risks of Model serving; performs only guided exercises. |
What changes from L1 to L2
Moving from Associate AI Platform Engineer to AI Platform Engineer means greater autonomy, complexity, scope, influence and evidence of impact—not simply more tools.
How readiness is shown
Sustained evidence of operating at L2-type scope: handles representative complexity, influences expected stakeholders and produces durable measurable outcomes.
How to prepare
Take on one assignment at next-level scope; seek feedback; document decisions/outcomes; mentor others where appropriate; close highest-priority skill gaps.
Adjacent careers
Computed from shared skills. It is a signal for exploring, not a hiring or eligibility guarantee.
- AI Governance & Responsible AIData & Analytics14 shared skills
- Data ArchitectureData & Analytics14 shared skills
- Analytics EngineeringData & Analytics12 shared skills
- Applied AI / Applied ScienceData & Analytics12 shared skills
- Data Governance & StewardshipData & Analytics9 shared skills
- Machine Learning EngineeringData & Analytics9 shared skills
Career framework v21, active since August 17, 2026.