🚀 AI/ML Platform Engineer — 6-Month Learning Roadmap

ai-ml-platform-engineer-6-month-learning-roadmap.md

🚀 AI/ML Platform Engineer — 6-Month Senior-Ready Roadmap

For Kubernetes/Cloud Engineers transitioning into AI/ML Engineering Updated with: Data Engineering foundations, Vector DBs, LLMOps, Distributed Training, Security & Governance, ML Observability Resources listed in the order you should follow them.

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📅 MONTH 1 — ML Foundations + Data Systems for ML

You can't be an ML Platform Engineer without understanding the data layer. Start both tracks in parallel.

Track A — ML Fundamentals

#	Resource	URL	Type
1	Andrew Ng — ML Specialization (theory foundations first)	https://www.coursera.org/specializations/machine-learning-introduction	Course (Free audit)
2	fast.ai — Practical Deep Learning for Coders (top-down, code-first)	https://course.fast.ai/	Course (Free)
3	fast.ai Book (companion, freely available online)	https://fastai.github.io/fastbook2e/	Book (Free)

Track B — Data Engineering for ML (Critical Layer — Don't Skip)

Modern ML platforms = feature pipelines + batch + streaming + lakehouse. Without this, you look like a model-serving engineer, not a platform engineer.

#	Resource	URL	Type
4	Apache Parquet & Arrow Fundamentals — columnar storage, the foundation	https://arrow.apache.org/docs/python/parquet.html	Docs
5	Delta Lake Docs — ACID ML data lakes (Databricks-native)	https://docs.delta.io/latest/index.html	Docs
6	Apache Iceberg Docs — open table format, broader engine support	https://iceberg.apache.org/docs/latest/	Docs
7	Apache Kafka Intro — event-driven data for ML feature pipelines	https://kafka.apache.org/intro	Docs
8	Confluent Kafka Docs — deeper Kafka reference	https://docs.confluent.io/kafka/introduction.html	Docs

🔁 Reframe Your Existing Work

• Write posts positioning your Kubernetes operators as ML infrastructure
• Your developer platforms = Internal ML Developer Portals
• Your API skills = ML serving gateways with rate limiting and queuing

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📅 MONTH 2 — MLOps Core + Tooling

Model Serving

#	Resource	URL	Type
9	KServe Docs — Kubernetes-native model serving (operator patterns you'll recognize)	https://kserve.github.io/website/docs/intro	Docs
10	KServe GitHub — source, examples, issues	https://github.com/kserve/kserve	GitHub
11	KServe Quickstart	https://kserve.github.io/website/docs/getting-started/quickstart-guide	Quickstart
12	Ray Serve Docs — alternative serving framework	https://docs.ray.io/en/latest/serve/index.html	Docs

Experiment Tracking & Pipelines

#	Resource	URL	Type
13	MLflow Docs — experiment tracking and model registry	https://mlflow.org/docs/latest/index.html	Docs
14	Kubeflow — ML workflows on Kubernetes	https://www.kubeflow.org/	Docs
15	Kubeflow Pipelines — portable, scalable ML workflows	https://www.kubeflow.org/docs/components/pipelines/overview/	Docs

Feature Stores

#	Resource	URL	Type
16	Feast Docs — open-source feature store	https://docs.feast.dev	Docs
17	Feast GitHub — source and examples	https://github.com/feast-dev/feast	GitHub

🔨 Month 2 Project

Train a simple model → serve it via KServe on Kind → expose via a Go API you write → log experiments with MLflow

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📅 MONTH 3 — LLM Infrastructure + Vector Databases

This is the highest-demand zone in 2026. RAG systems are production reality. Interviewers will probe deeply here.

LLM Serving Engines

#	Resource	URL	Type
18	vLLM Docs — high-throughput LLM serving with paged attention	https://docs.vllm.ai/en/stable/	Docs
19	vLLM Quickstart	https://docs.vllm.ai/en/latest/getting_started/quickstart/	Quickstart
20	Ollama — run open-source LLMs locally and on-cluster	https://ollama.com/	Tool

Vector Databases (Don't Skip — Interviewers Always Ask)

You need to understand: embedding pipelines, chunking strategy, vector indexing (HNSW, IVF), recall vs latency tradeoffs.

#	Resource	URL	Type
21	Milvus Docs — enterprise-scale open-source vector DB, Kubernetes-native	https://milvus.io/docs	Docs
22	Weaviate Docs — hybrid search + knowledge graph capabilities	https://weaviate.io/developers/weaviate	Docs
23	Pinecone Docs — managed vector DB, easiest to start with	https://docs.pinecone.io/home	Docs
24	Qdrant Docs — performance-focused OSS vector DB, good for self-hosting	https://qdrant.tech/documentation/	Docs

GPU Kubernetes Operations

#	Resource	URL	Type
25	NVIDIA GPU Operator — GPU node management on Kubernetes	https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/index.html	Docs
26	KEDA Docs — event-driven autoscaling on GPU utilization metrics	https://keda.sh/docs/latest/	Docs
27	KEDA GitHub	https://github.com/kedacore/keda	GitHub

Model Quantization (Conceptual Depth)

#	Resource	URL	Type
28	HuggingFace Quantization Guide — GGUF, AWQ, GPTQ explained	https://huggingface.co/docs/transformers/main/en/quantization/overview	Docs

🔨 Month 3 Project

Deploy a self-hosted LLM (Llama/Mistral) + a vector store (Milvus) + autoscaling via KEDA + Go API with request queuing This is the foundation of your capstone RAG platform

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📅 MONTH 4 — LLMOps, Distributed Training + Platform Architecture

LLMOps & Prompt Infrastructure (2026 Hiring Signal)

Prompt versioning, evaluation frameworks, guardrails — these are now standard interview topics.

#	Resource	URL	Type
29	LangChain Docs — build LLM chains, agents, and RAG pipelines	https://python.langchain.com/docs/introduction/	Docs
30	LangSmith Docs — prompt versioning, tracing, and evaluation	https://docs.smith.langchain.com/	Docs
31	LlamaIndex Docs — data framework for LLM retrieval (RAG-focused)	https://docs.llamaindex.ai/en/stable/	Docs
32	Weights & Biases — LLM Evals	https://wandb.ai/site/solutions/llm	Docs
33	Guardrails AI — output validation and guardrails for LLMs	https://www.guardrailsai.com/docs	Docs

Distributed Training (Senior-Level Requirement)

You're inference-heavy so far. Add this to cover senior platform roles that own the full training → serving lifecycle.

#	Resource	URL	Type
34	PyTorch Distributed Training Overview — data, tensor, pipeline parallelism explained	https://pytorch.org/tutorials/beginner/dist_overview.html	Docs
35	Ray Train Docs — distributed ML training (pairs with Ray Serve you already know)	https://docs.ray.io/en/latest/train/train.html	Docs
36	Ray Train + PyTorch Quickstart	https://docs.ray.io/en/latest/train/getting-started-pytorch.html	Quickstart

AI Platform Architecture (Engineering Blog Reading)

#	Resource	URL	Type
37	Uber — Michelangelo ML Platform	https://www.uber.com/blog/michelangelo-machine-learning-platform/	Blog
38	Airbnb — ML Platform Architecture	https://medium.com/airbnb-engineering/using-machine-learning-to-predict-value-of-homes-on-airbnb-9272d3d4739d	Blog
39	LinkedIn — Scaling ML Productivity	https://engineering.linkedin.com/blog/2019/01/scaling-machine-learning-productivity-at-linkedin	Blog
40	Netflix — Metaflow Open Source	https://netflixtechblog.com/open-sourcing-metaflow-a-human-centric-framework-for-data-science-fa72e04a5d9	Blog

Kubernetes Operator Development for ML

#	Resource	URL	Type
41	Kubebuilder Book — build operators (review ML-specific patterns)	https://book.kubebuilder.io/	Book (Free)
42	Operator SDK Docs	https://sdk.operatorframework.io/docs/	Docs

GPU Cost Optimization

#	Resource	URL	Type
43	AWS EC2 Spot Instances for ML	https://aws.amazon.com/blogs/machine-learning/run-machine-learning-workloads-with-amazon-ec2-spot-instances-and-amazon-ec2-auto-scaling/	Blog
44	GCP Spot VMs for AI Workloads	https://cloud.google.com/compute/docs/instances/spot	Docs
45	NVIDIA MIG (Multi-Instance GPU)	https://docs.nvidia.com/datacenter/tesla/mig-user-guide/	Docs

🔨 Month 4 Project

Build a Golang Kubernetes Operator for ModelServer: manages model lifecycle (versioning, canary rollouts, shadow mode) Add LangSmith-based prompt evaluation to your RAG pipeline from Month 3

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📅 MONTH 5 — Cloud AI Services + ML Observability + Security & Governance

LLM-Specific Observability (Senior-Level Signals)

Prometheus + Grafana alone is insufficient for AI roles. Interviewers will ask about: token throughput, latency percentiles, hallucination rate tracking, embedding drift.

#	Resource	URL	Type
46	Arize AI — LLM observability with embedding drift, hallucination monitoring	https://docs.arize.com/arize	Docs
47	WhyLabs Docs — ML monitoring for data drift and model performance	https://docs.whylabs.ai/docs/	Docs
48	Evidently AI — open-source ML monitoring and drift detection	https://docs.evidentlyai.com/	Docs
49	Prometheus Docs — infrastructure metrics (token throughput, GPU utilization)	https://prometheus.io/docs/introduction/overview/	Docs
50	Grafana Docs — dashboarding for ML-specific metrics	https://grafana.com/docs/grafana/latest/	Docs

ML Security & Governance (Big Gap Interviewers Now Probe)

Model supply chain security, PII handling, data lineage, RBAC for model access — platform engineers who know this stand out.

#	Resource	URL	Type
51	ML Security OWASP Top 10 for LLMs — the canonical security reference for LLM apps	https://owasp.org/www-project-top-10-for-large-language-model-applications/	Docs
52	Sigstore — Model Signing — supply chain security for ML artifacts	https://www.sigstore.dev/	Docs
53	OPA (Open Policy Agent) — RBAC and policy enforcement for model access	https://www.openpolicyagent.org/docs/latest/	Docs
54	OpenLineage — data lineage standard for ML pipelines	https://openlineage.io/	Docs
55	Marquez — metadata and lineage service built on OpenLineage	https://marquezproject.ai/	Docs

Cloud AI Services

Google Cloud (Primary)

#	Resource	URL	Type
56	Vertex AI Documentation	https://cloud.google.com/vertex-ai/docs	Docs
57	GCP ML Engineer Learning Path	https://www.skills.google/paths/17	Course (Free)
58	Preparing for GCP ML Eng Cert — Coursera	https://www.coursera.org/professional-certificates/preparing-for-google-cloud-machine-learning-engineer-professional-certificate	Course

AWS (Secondary)

#	Resource	URL	Type
59	Amazon SageMaker Developer Guide	https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html	Docs
60	Amazon Bedrock Docs	https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html	Docs

Azure (Secondary)

#	Resource	URL	Type
61	Azure Machine Learning Docs	https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning	Docs

🎯 Certification Strategy

Senior roles care more about OSS contributions and technical blog depth than certs. Treat the cert as a bonus, not the goal.

#	Resource	URL	Notes
62	Google Professional ML Engineer (primary — most respected)	https://cloud.google.com/learn/certification/machine-learning-engineer	$200
63	Exam Guide PDF	https://cloud.google.com/learn/certification/guides/machine-learning-engineer	Free
64	AWS ML Specialty (optional secondary)	https://aws.amazon.com/certification/certified-machine-learning-specialty/	$300

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📅 MONTH 6 — Capstone Project + Portfolio + Job Targeting

🏆 Capstone: Production-Grade RAG Platform on Kubernetes

One cohesive system beats four disconnected demos. This single project will outperform most ML engineer portfolios.

System components to build:

Component	Technology
Data ingestion pipeline	Kafka + Iceberg/Delta Lake
Embedding generation service	HuggingFace models + vLLM
Vector database	Milvus (Kubernetes-native)
LLM inference	vLLM with quantized Llama/Mistral
Autoscaling	KEDA on GPU utilization + queue depth
Model lifecycle management	Your custom Golang Kubernetes Operator
Prompt evaluation & tracing	LangSmith or W&B
Observability dashboards	Prometheus + Grafana + Arize AI (embedding drift)
Canary model rollout	Istio traffic splitting via your Operator
Security & access control	OPA policies for model endpoint RBAC
Data lineage	OpenLineage integration
Cost monitoring	GPU utilization reports + spot instance optimization

Job Targeting Resources

#	Resource	URL	Type
65	ML Engineer jobs — LinkedIn	https://www.linkedin.com/jobs/search/?keywords=ML+Platform+Engineer	Job Board
66	ai-jobs.net — AI-specific job board	https://ai-jobs.net/	Job Board
67	Levels.fyi — ML Engineer salaries	https://www.levels.fyi/t/machine-learning-engineer	Salary Data

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🗂️ BONUS: Reference Resources (Use Throughout All 6 Months)

Resource	URL	Why
HuggingFace — model hub, datasets, spaces	https://huggingface.co/	Central hub for open-source models
Papers With Code — ML research + reproducible code	https://paperswithcode.com/	Stay current on research
The Batch (Andrew Ng newsletter)	https://www.deeplearning.ai/the-batch/	Weekly AI news digest
MLOps Community	https://mlops.community/	Networking with practitioners
CNCF AI/ML Working Group	https://github.com/cncf/tag-runtime/blob/main/wg/artificial-intelligence.md	Kubernetes + ML community
Golang Kubernetes client-go	https://github.com/kubernetes/client-go	Operator development base
Apache Arrow Docs	https://arrow.apache.org/docs/	Columnar data format fundamentals

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✅ Senior-Ready Portfolio Checklist (Month 6)

Projects

• Capstone RAG Platform — full production stack (see Month 6 table above)
• Golang Kubernetes Operator for ModelServer (versioning, canary, shadow mode)
• End-to-end ML pipeline — Kafka → Iceberg → training → model registry → serving
• Distributed training demo — PyTorch + Ray Train on multi-GPU Kubernetes node pool

Content

• 3+ technical blog posts framing infra skills through the ML lens
• 1 OSS contribution to KServe, Kubeflow, or Milvus (beats any cert)
• LinkedIn posts on GPU autoscaling, LLM serving, or MLOps — this space has low-quality content, you'll stand out

Interview Readiness

• Can explain RAG architecture end-to-end (chunking → embedding → retrieval → generation)
• Can articulate data parallelism vs tensor parallelism vs pipeline parallelism
• Can discuss HNSW vs IVF vector indexing tradeoffs
• Can explain ML supply chain security threats (model poisoning, prompt injection)
• Can design RBAC for a multi-tenant model serving platform

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🗺️ Revised Learning Flow (Mirrors Real ML System Evolution)

Month 1: ML Fundamentals + Data Systems (Kafka, Iceberg, Delta Lake)
         ↓
Month 2: MLOps (KServe, Kubeflow, Feast, MLflow)
         ↓
Month 3: LLM Infra (vLLM, Vector DBs, GPU ops)
         ↓
Month 4: LLMOps + Distributed Training + Platform Architecture
         ↓
Month 5: Observability + Security + Cloud AI Services
         ↓
Month 6: Capstone Project + Portfolio + Job Search

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Your unfair advantage: Most ML engineers are learning Kubernetes. You already own it. Add the ML domain layer on top and you become the rarest hire in the market — a platform engineer who also speaks ML.