MLOps / LLMOps Engineer — Inference Phase

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Inference Specialization

MLOps / LLMOps Engineer

The backbone of every production AI system. LinkedIn reports 9.8× growth in 5 years. MLOps engineers build pipelines, platforms, and infrastructure keeping models reliable at scale. One of the fastest-growing roles in tech.

Days

Projects

$187K

Median

9.8×

5-Year Growth

$140K–$260K

Salary Range

Week 1: ML Pipelines and Experiment Infrastructure · Days 1–8  

Build the tracking and pipeline infrastructure that converts notebooks into reproducible, versioned systems.

D1–3

MLflow, DVC, and reproducible experiment lifecycle

Version models, datasets, and parameters. Track 50 runs, reproduce any experiment, register and promote models

↗ ML Engineering for Production — Coursera (free audit)

D4–6

Apache Airflow and Kubeflow Pipelines for ML

ML workflow orchestration as DAGs. Kubeflow for Kubernetes-native training pipelines at enterprise scale

↗ Apache Airflow official documentation (free)

D7

Data versioning, lineage, and quality gates

DVC for data, Great Expectations for automated data quality, OpenLineage for lineage tracking

↗ DVC getting started guide (free)

🏗 Project — Day D8: Automated ML Retraining Pipeline 

· Airflow DAG with 6 or more tasks: fetch, validate, train, evaluate, promote, deploy

· Evidently drift detector as a trigger condition

· Model promotion and rollback logic

· Slack or email failure notification

Week 2: Kubernetes, Model Serving, and Observability · Days 9–24  

Deploy and autoscale models on Kubernetes. Master LLM serving with vLLM and build production-grade monitoring.

D9–12

Kubernetes for ML: KServe, Seldon, BentoML

Model serving on Kubernetes — autoscaling, canary deployments, GPU scheduling, zero-downtime updates

↗ KServe official documentation (free)

D13–16

GPU cluster management and cost optimisation

NVIDIA DCGM, K8s device plugins, spot instances, GPU bin-packing for 40%+ cost reduction

↗ NVIDIA GPU Operator documentation (free)

D17–20

LLMOps: vLLM, TGI, and LLM inference servers

Text Generation Inference, vLLM PagedAttention, continuous batching — optimise cost per token at scale

↗ vLLM official documentation (free)

D21–23

Advanced drift monitoring and business KPI alignment

Evidently for statistical drift detection, linking model metrics to revenue and user engagement outcomes

↗ Evidently AI full documentation (free)

🏗 Project — Day D24: Multi-Model Serving Platform on Kubernetes 

· 3 different model types served on Kubernetes via KServe

· Prometheus and Grafana monitoring dashboards

· A/B traffic splitting at 10% vs 90%

· Load test showing stability at 1000 RPS

Week 3: Feature Stores, Platform Engineering, Advanced MLOps · Days 25–40  

Build the full ML platform: feature stores, self-service tooling for data scientists, and cost attribution systems.

D25–28

Feature stores: Feast, Tecton, and Hopsworks

Production feature platform with batch and online serving, versioning, governance, under 10ms online retrieval

↗ Feast feature store documentation (free)

D29–32

ML platform engineering and internal developer experience

Self-service notebooks, GPU quota management, job scheduling, and internal developer portals for ML teams

↗ Kubeflow platform documentation (free)

D33–36

FinOps for ML: cost attribution and chargeback

Tracking GPU costs per team and experiment. Building a live cost dashboard. Cloud spend optimisation tactics

↗ FinOps Foundation learning resources (free)

D37–39

ML governance: model registry, lineage, and audit trails

Regulatory compliance for model deployment — audit history, approval workflows, model versioning policies

↗ MLflow Model Registry documentation (free)

🏗 Project — Day D40: Full Enterprise ML Platform — Capstone 

· Integrated 5-component platform: pipelines, serving, monitoring, feature store, governance

· Operator runbook for on-call engineers

· Performance benchmark vs baseline infrastructure

· Cost analysis and optimisation report with savings estimate

· Full architecture diagram