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MLOps Engineer

TechnicalFast GrowingHiring since 2023

Bridges ML model development and production by building automated pipelines for training, deploying, monitoring, and scaling with proper versioning and CI/CD. The GenAI wave has multiplied demand — LinkedIn reports 9.8x growth over 5 years. Banks, e-commerce, and SaaS companies in Australia and Singapore are the largest APAC employers. Median salary: $151K-$183K.

Salary Range
$120K–$200K
Hiring Companies
Netflix · Grab · Atlassian
Hot Regions
USAAustraliaSingaporeIndiaU.K.China
Core Skills
ML Pipeline · K8s · Model deployment

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DevOps BootcampIntermediate · 11 weeks · K8s + CI/CD + Cloud deployment
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Job Description Analysis

Core Responsibilities

  • CI/CD pipeline for building and maintaining ML models - a fully automated process from training and validation to deployment and monitoring
  • Manage model version control, experiment tracking, and model registries (MLflow, Weights & Biases)
  • Design the infrastructure of ML services: GPU cluster management, model inference service, automatic expansion and contraction
  • Implement model monitoring: data drift detection, performance degradation alarm, A/B testing framework
  • Optimize inference performance and cost - model quantization, distillation, caching strategies

Requirements

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  1. 3+ years of DevOps/SRE experience, proficient in Kubernetes, Docker, Terraform
  2. Familiar with at least one ML platform (SageMaker, Vertex AI, Azure ML, Kubeflow)
  3. Master model deployment technologies: TorchServe, Triton, vLLM, ONNX Runtime
  4. Understand the ML training process and common frameworks (PyTorch, TensorFlow)
  5. Proficient in Python and Shell scripting, experience with CI/CD tools (GitHub Actions, Jenkins)
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Nice to Have

  • Experience in GPU cluster management and distributed training
  • Understand LLM inference optimization technology (KV Cache, Speculative Decoding)
  • Experience with cost optimization (Spot Instances, hybrid cloud strategies)

A Day in the Life

  • Morning: Check the nightly running status of the model deployment pipeline and fix failed automation tasks
  • Morning: Review the new model version submitted by the data science team, and trigger grayscale release after verifying that the performance indicators meet the standards.
  • Afternoon: Optimize the GPU utilization of the inference service, adjust batch size and concurrency strategy to reduce costs
  • Afternoon: Configure new data drift monitoring rules and add alarm thresholds to the credit risk control model
  • Evening: Upgrade the Kubernetes cluster and ML platform components in preparation for the new model to go online next week

Career Transition Guide

Suitable Backgrounds

  • DevOps/SRE Engineer: Infrastructure and automation capabilities are directly applicable, just supplement ML knowledgeSee this career today: DevOps Engineer · Site Reliability Engineer
  • Back-end engineer: Experience with microservices and APIs, quick to learn ML deployment linksSee this career today: Software Engineer
  • Data Engineer: Familiar with data pipelines, it is a natural extension to expand to model pipelinesSee this career today: Data Engineer
  • Data Scientist: If you want to go deeper into engineering implementation, MLOps is the best directionSee this career today: Senior Data Scientist

Transition Path

  1. Step 1: Learn the basics of ML - understand the complete process of training, verification, and deployment (no in-depth algorithm required)
  2. Step 2: Master MLflow for experiment tracking and model version management
  3. Step 3: Deploy an ML inference service on Kubernetes to achieve automatic expansion and contraction
  4. Step 4: Build a complete ML CI/CD pipeline: code submission → training → verification → deployment → monitoring
  5. Step 5: Learn the unique MLOps challenges of LLM (foundation model deployment, vLLM, inference optimization) and submit your job

Learning Roadmap

Phase 1: ML Basics + Tools (January-February)

Learn the basic process of training and evaluating ML modelsMaster MLflow: experiment tracking, model registration, model serviceUnderstand the application of Docker and Kubernetes in ML scenarios

Phase 2: Pipeline Construction (February-April)

Build an end-to-end ML CI/CD pipeline (GitHub Actions + MLflow + K8s)Learning model deployment technology: TorchServe, Triton Inference ServerImplement data drift detection and model performance monitoringMaster GPU resource management and cost optimization strategies

Phase 3: LLM operation and maintenance (April-June)

Learn LLM inference optimization: vLLM, quantization, KV CacheMaster the advanced capabilities of cloud ML platforms (SageMaker or Vertex AI)Build an observability and cost governance system for LLM applications

Common Mistakes

Wrong

I think MLOps is DevOps with an ML prefix and the same methodology.

Right

ML systems have unique challenges: data version control, experiment tracking, and model drift monitoring all require specialized tools

Wrong

Only focus on deployment automation and ignore continuous monitoring after the model goes online

Right

Model deployment is just the beginning - production problems such as data drift and performance degradation are the core value of MLOps

Wrong

Trying to master both ML algorithms and MLOps engineering

Right

MLOps engineers focus on engineering capabilities. They only need to understand the basic ML process and do not need to design models themselves.

Recommended Resources

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