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 L2 · AI Platform Engineer
Apply ai platform & mlops engineering capability at independently delivers standard work within a team or defined domain.
The five dimensions that change
- Autonomy
- Independent routine ownership
- Scope
- Independently delivers standard work within a team or defined domain.
- Complexity
- Moderate
- Influence
- Immediate team
- Business impact
- Task/team
- Ambiguity
- Moderate
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 L2.
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 L2 scope.
Skills expected at L2
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.
Human8
| Human | P | Supporting |
|---|---|---|
| Clear verbal communicationCommunication & Language | P2FoundationalSupporting | Applies Clear verbal communication to routine situations using established methods and controls. |
| Clear written communicationCommunication & Language | P2FoundationalSupporting | Applies Clear written communication to routine situations using established methods and controls. |
| CollaborationCollaboration & Relationships | P2FoundationalSupporting | Applies Collaboration to routine situations using established methods and controls. |
| Critical thinkingThinking & Problem Solving | P2FoundationalSupporting | Applies Critical thinking to routine situations using established methods and controls. |
| Cross-functional collaborationCollaboration & Relationships | P2FoundationalSupporting | Applies Cross-functional collaboration to routine situations using established methods and controls. |
| Learning agilityExecution & Self-Management | P2FoundationalSupporting | Applies Learning agility to routine situations using established methods and controls. |
| Stakeholder alignmentInfluence & Leadership Without Authority | P2FoundationalSupporting | Applies Stakeholder alignment to routine situations using established methods and controls. |
| Structured problem solvingThinking & Problem Solving | P2FoundationalSupporting | Applies Structured problem solving to routine situations using established methods and controls. |
Professional6
| Professional | P | Supporting |
|---|---|---|
| AI lifecycle governanceData & Analytics Practice | P2FoundationalCore | Applies AI lifecycle governance to routine situations using established methods and controls. |
| AI model governanceData & Analytics Practice | P2FoundationalCore | Applies AI model governance to routine situations using established methods and controls. |
| Architecture governanceIT Service Management & Governance | P2FoundationalSupporting | Applies Architecture governance to routine situations using established methods and controls. |
| Model risk managementData & Analytics Practice | P2FoundationalCore | Applies Model risk management to routine situations using established methods and controls. |
| Responsible AI principlesData & Analytics Practice | P2FoundationalSupporting | Applies Responsible AI principles to routine situations using established methods and controls. |
| Technology governanceIT Service Management & Governance | P2FoundationalSupporting | Applies Technology governance to routine situations using established methods and controls. |
Technical10
| Technical | P | Supporting |
|---|---|---|
| AI observabilityData Science, ML & AI | P2FoundationalSupporting | Uses AI observability for routine tasks with documented patterns and review. |
| Feature pipeline engineeringData Science, ML & AI | P2FoundationalSupporting | Uses Feature pipeline engineering for routine tasks with documented patterns and review. |
| Feature store engineeringData Science, ML & AI | P2FoundationalSupporting | Uses Feature store engineering for routine tasks with documented patterns and review. |
| ML experiment trackingData Science, ML & AI | P2FoundationalSupporting | Uses ML experiment tracking for routine tasks with documented patterns and review. |
| MLOpsData Science, ML & AI | P2FoundationalCore | Uses MLOps for routine tasks with documented patterns and review. |
| MLflowData Science, ML & AI | P2FoundationalCore | Uses MLflow for routine tasks with documented patterns and review. |
| Model deploymentData Science, ML & AI | P2FoundationalCore | Uses Model deployment for routine tasks with documented patterns and review. |
| Model monitoringData Science, ML & AI | P2FoundationalCore | Uses Model monitoring for routine tasks with documented patterns and review. |
| Model registryData Science, ML & AI | P2FoundationalSupporting | Uses Model registry for routine tasks with documented patterns and review. |
| Model servingData Science, ML & AI | P2FoundationalCore | Uses Model serving for routine tasks with documented patterns and review. |
What changes from L2 to 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.
How readiness is shown
Sustained evidence of operating at L3-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.