Machine Learning Platform Engineer Resume Example
Machine Learning Platform Engineer hiring managers look for credible ownership of enterprise ML infrastructure: feature stores, registries, training, and serving. ML Platform Engineer with 6+ years architecting enterprise ML infrastructure — feature stores, model registries, distributed training, and real-time serving pipelines.
Study how the example ties Python, Kubeflow, Ray Train, Ray Serve, MLflow to delivery evidence, then rewrite with your verified timeline and stack.
Machine Learning Platform Engineer Resume Sample
Sanjay Iyer
Machine Learning Platform Engineer
Bengaluru, Karnataka · sanjay.iyer@email.com · +91-9900221133 · linkedin.com/in/sanjayiyer-mlpe
Professional Summary
ML Platform Engineer with 6+ years architecting enterprise ML infrastructure — feature stores, model registries, distributed training, and real-time serving pipelines. Scaled ML platform from 5 models in production to 200+ while reducing model deployment cycle from 3 weeks to 6 hours.
Machine Learning Platform Engineer Technical Skills
Core Skills: Python · Kubeflow · Ray Train · Ray Serve · MLflow · Feast · Tecton · Seldon Core · BentoML · Triton · Spark · Kafka · Kubernetes · Helm · ArgoCD · Terraform · AWS SageMaker · Feature Engineering · A/B Testing · Monitoring (Evidently AI)
Professional Experience
- Built Tecton-based feature platform serving 800+ features to 50+ production ML models at <10ms p99 latency for real-time ride matching and surge pricing.
- Designed Ray Train distributed training cluster on EKS supporting multi-GPU and CPU workloads — enabling 10x larger models without engineering intervention.
- Built internal ML model registry with automated evaluation gates, champion-challenger testing, and A/B experiment tracking — reducing deployment approval cycle from 3 weeks to 6 hours.
- Implemented model monitoring using Evidently AI detecting feature drift and prediction distribution shifts, auto-alerting teams within 15 minutes of degradation.
- Created cost attribution system tracking GPU/CPU spend per team and per model — enabling FinOps decisions that saved ₹1.8Cr in quarterly training costs.
- Onboarded 60+ data scientists to platform through workshops, runbooks, and self-service tooling — growing production model count 40x in 24 months.
- Deployed MLflow + Kubeflow Pipelines-based MLOps stack for 3 client engagements spanning retail, banking, and telecom.
- Built Airflow-orchestrated retraining pipelines for 10 production ML models with automatic rollback on performance regression.
- Developed Seldon Core deployment wrappers standardizing REST inference endpoints with built-in canary release support.
Machine Learning Platform Engineer Projects
Grafana + Prometheus dashboard attributing GPU/CPU compute costs by team, model, and experiment — integrated with Kubernetes resource accounting.
Education
M.Tech Computer Science (AI) — IIIT Bangalore, 2018 | CGPA: 8.8/10
Certifications
- AWS Certified Machine Learning – Specialty
- Certified Kubernetes Administrator (CKA)
- Databricks Certified ML Professional
All details in this resume example are illustrative and should be replaced with your actual experience, achievements, education, and certifications.
Practical Machine Learning Platform Engineer resume guidance focused on enterprise ML infrastructure: feature stores, registries, training, and serving, using only claims you can verify from your own history.
How to Write a Machine Learning Platform Engineer Resume
Interviewers need proof of enterprise ML infrastructure: feature stores, registries, training, and serving, not an undifferentiated cloud of neighboring tools.
Ground depth in Python, Kubeflow, Ray Train, Ray Serve, MLflow by linking each skill to a responsibility from your summary, experience, or ML Platform Cost Dashboard.
Resumes stumble when they MLOps Engineer feature-model work without platform product ownership. Keep every technology claim tied to something you personally owned.
Prefer decision language—what you modeled, operated, secured, led, or shipped—over tool inventories that could fit any adjacent title.
Close the loop by showing how Kubeflow/Ray/MLflow platforms with Kubernetes-backed ML operations appears in your bullets, projects, and summary without inventing employers, percentages, or scale.
Experienced professional skilled in many modern tools related to machine learning platform engineer.
Built Tecton-based feature platform serving 800+ features to 50+ production ML models at <10ms p99 latency for real-time ride matching and surge pricing.
What to Include in a Machine Learning Platform Engineer Resume
Cover Python, Kubeflow, Ray Train, Ray Serve, MLflow, Feast, Tecton, Seldon Core when truthful, grouped the way you actually practiced the work rather than as a buzzword dump.
Add AWS Certified Machine Learning – Specialty, Certified Kubernetes Administrator (CKA), or Databricks Certified ML Professional only if completed, preserving official credential names.
Include ML Platform Cost Dashboard with technologies such as Python, Kubeflow, Ray Train, Ray Serve when you need compact proof alongside employment bullets. Add a certifications subsection because this source includes AWS Certified Machine Learning – Specialty; Certified Kubernetes Administrator (CKA); Databricks Certified ML Professional; on your resume, list only credentials you actually hold and preserve their official names.
Machine Learning Platform Engineer Resume Summary Example
Begin with 6 years centered on enterprise ML infrastructure: feature stores, registries, training, and serving, then reinforce the strongest theme already present in the professional summary.
ML Platform Engineer with 6+ years architecting enterprise ML infrastructure — feature stores, model registries, distributed training, and real-time serving pipelines. Scaled ML platform from 5 models in production to 200+ while reducing model deployment cycle from 3 weeks to 6 hours.
Important Machine Learning Platform Engineer Skills for a Resume
Core Skills
Python · Kubeflow · Ray Train · Ray Serve · MLflow · Feast · Tecton · Seldon Core · BentoML · Triton · Spark · Kafka · Kubernetes · Helm · ArgoCD · Terraform · AWS SageMaker · Feature Engineering · A/B Testing · Monitoring (Evidently AI)
Retain Machine Learning Platform Engineer skills you can defend with a delivery story, design choice, incident, test, leadership example, or project walkthrough.
Machine Learning Platform Engineer Resume Experience Examples
Senior ML Platform Engineer
Built Tecton-based feature platform serving 800+ features to 50+ production ML models at <10ms p99 latency for real-time ride matching and surge pricing.
Senior ML Platform Engineer
Designed Ray Train distributed training cluster on EKS supporting multi-GPU and CPU workloads — enabling 10x larger models without engineering intervention.
Senior ML Platform Engineer
Implemented model monitoring using Evidently AI detecting feature drift and prediction distribution shifts, auto-alerting teams within 15 minutes of degradation.
MLOps / Data Engineer
Deployed MLflow + Kubeflow Pipelines-based MLOps stack for 3 client engagements spanning retail, banking, and telecom.
Use real numbers when you can verify them. Do not invent metrics simply to make the resume sound stronger.
Machine Learning Platform Engineer ATS Keywords
Choose keywords that match both the Machine Learning Platform Engineer job description and work you can substantiate. Spell out important concepts naturally in summary and experience instead of pasting this list.
Machine Learning Platform Engineer Resume Tips
Lead with platform components
Open with feature stores, registries, or serving stacks you built.
Show orchestration
Connect Kubeflow, Ray, ArgoCD, or Terraform to platform workflows.
Place serving carefully
Mention Seldon, BentoML, Triton, or SageMaker only when used.
Differentiate from MLOps/ML Engineer
Emphasize platform infrastructure over single-model delivery.
No invented model counts
Avoid fabricated training-job or model-fleet metrics.
Calibrate claims
If a metric, employer, or certification is missing from your history, omit it rather than borrowing sample details.
Frequently Asked Questions
How do I prove ownership of enterprise ML infrastructure: feature stores, registries, training, and serving on a Machine Learning Platform Engineer resume?
Cover enterprise ML infrastructure: feature stores, registries, training, and serving with source-backed skills such as Python, Kubeflow, Ray Train, Ray Serve, MLflow, plus experience or projects that show what you personally owned.
Which Machine Learning Platform Engineer skills belong in the skills section?
Prioritize Python, Kubeflow, Ray Train, Ray Serve, MLflow and other category skills only when you can explain them with a project, production example, or troubleshooting story.
What should the Machine Learning Platform Engineer summary emphasize?
State about 6 years of Machine Learning Platform Engineer work and the Kubeflow/Ray/MLflow platforms with Kubernetes-backed ML operations focus that matches the job description—only if that tenure is true for you.
Can ML Platform Cost Dashboard support a thin experience section?
Yes—ML Platform Cost Dashboard can support claims involving Python, Kubeflow, Ray Train, Ray Serve when you need concise, technology-specific project evidence.
Should I list credentials such as AWS Certified Machine Learning – Specialty, Certified Kubernetes Administrator (CKA), on a Machine Learning Platform Engineer resume?
AWS Certified Machine Learning – Specialty, Certified Kubernetes Administrator (CKA), or Databricks Certified ML Professional belongs on the resume only when earned; otherwise rely on skills and delivery evidence.
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