MLOps Engineer IT & Data Development

01.tech; Victoria; Remote; AUD 140,000 - 190,000; Full time

Benefits offered by this job

Annual benefits allowance

Relocation assistance after probation

Corporate digital library

Weekly online sessions with a mental-w

Job summary

01.tech is seeking a skilled MLOps Engineer to design, build, and maintain a scalable ML platform. You will automate the full model lifecycle—from training and validation to deployment, monitoring, and retraining—and develop AutoML infrastructure for experiments, tuning, and production promotions.

You will implement MLflow, build a feature store, and orchestrate pipelines with Airflow for batch and real-time scoring.

Qualifications

• Hands-on experience building or operating MLOps infrastructure or ML platforms.
• Strong Python and Linux skills, including infrastructure automation and development of supporting services.
• Experience with Docker and a solid understanding of Kubernetes and container orchestration.
• Experience building CI/CD pipelines and managing infrastructure using Terraform, Ansible, or similar tools.
• Solid understanding of the full ML model lifecycle, including training, validation, registration, deployment, monitoring, and retraining.
• Experience with MLflow, Airflow, Feature Stores, or comparable technologies.
• Experience implementing monitoring, logging, alerting, and troubleshooting for production systems.
• Understanding of batch scoring and real-time inference architectures.
• Experience integrating services with message brokers and analytical data stores.
• Experience building AutoML platforms and automating experiments using Optuna or Ray Tune.
• Hands-on experience deploying and operating LLMs or building LLMOps infrastructure.

Responsibilities

• Design, build, and maintain an ML platform that automates the entire model lifecycle, from training and validation to deployment, monitoring, and retraining
• Develop AutoML infrastructure to automate experiments, hyperparameter tuning, model evaluation, and promotion from staging to production
• Implement and maintain MLflow or similar tools for experiment tracking, model versioning, and reproducibility
• Design and maintain a Feature Store with online and offline storage, ensuring point-in-time correctness and feature consistency between training and inference
• Automate training, retraining, and scoring pipelines using Airflow and other orchestration tools, supporting both batch scoring and real-time inference
• Build CI/CD pipelines for ML, including automated data and model testing, quality checks, safe deployments, and rollback mechanisms
• Implement production monitoring for ML systems, covering availability, latency, throughput, resource utilization, data drift, concept drift, and model performance
• Containerize, deploy, and scale ML services using Docker and Kubernetes, ensuring reliability and fault tolerance
• Develop Infrastructure as Code (IaC) practices and manage environments, configurations, secrets, and access using Terraform, Ansible, Vault, and similar tools
• Work closely with Data Engineering to integrate the ML platform with ClickHouse, Kafka, PostgreSQL, Redis, and other data infrastructure components
• Establish MLOps standards, reusable pipeline templates, and tooling that streamline model development, testing, and operations for Data Science and ML Engineering teams

Skills

MLOps

Python

Linux

Docker

Kubernetes

CI/CD

Terraform

Ansible

Airflow

MLflow

vLLM

Ollama

LiteLLM

Data streaming

Tools

Docker

Kubernetes

Terraform

Ansible

Airflow

MLflow

ClickHouse

Kafka

Job description

Responsibilities

• Design, build, and maintain an ML platform that automates the entire model lifecycle, from training and validation to deployment, monitoring, and retraining
• Develop AutoML infrastructure to automate experiments, hyperparameter tuning, model evaluation, and promotion from staging to production
• Implement and maintain MLflow or similar tools for experiment tracking, model versioning, and reproducibility
• Design and maintain a Feature Store with online and offline storage, ensuring point-in-time correctness and feature consistency between training and inference
• Automate training, retraining, and scoring pipelines using Airflow and other orchestration tools, supporting both batch scoring and real-time inference
• Build CI/CD pipelines for ML, including automated data and model testing, quality checks, safe deployments, and rollback mechanisms
• Implement production monitoring for ML systems, covering availability, latency, throughput, resource utilization, data drift, concept drift, and model performance
• Containerize, deploy, and scale ML services using Docker and Kubernetes, ensuring reliability and fault tolerance
• Develop Infrastructure as Code (IaC) practices and manage environments, configurations, secrets, and access using Terraform, Ansible, Vault, and similar tools
• Work closely with Data Engineering to integrate the ML platform with ClickHouse, Kafka, PostgreSQL, Redis, and other data infrastructure components
• Establish MLOps standards, reusable pipeline templates, and tooling that streamline model development, testing, and operations for Data Science and ML Engineering teams

LLMOps & LLM Infrastructure

• Build and maintain infrastructure for deploying and operating LLM services, including environment setup, configuration management, and model versioning
• Configure and optimize LLM inference using vLLM, Ollama, and similar tools, focusing on GPU utilization, batching, latency, throughput, and memory efficiency
• Provide unified API access to multiple LLMs through LiteLLM or equivalent solutions, implementing request routing, fallback mechanisms, and usage controls
• Integrate LLM services with internal applications and platforms, including Onyx
• Automate LLM service deployments and updates, ensuring reproducible configurations, model versioning, and controlled releases
• Implement LLM infrastructure monitoring and optimize inference performance, operating costs, scalability, and reliability
• Integrate LLM services with existing ML pipelines and platform tooling when needed

Requirements

• Hands-on experience building or operating MLOps infrastructure or ML platforms
• Strong Python and Linux skills, including infrastructure automation and development of supporting services
• Experience with Docker and a solid understanding of Kubernetes and container orchestration
• Experience building CI/CD pipelines and managing infrastructure using Terraform, Ansible, or similar tools
• Solid understanding of the full ML model lifecycle, including training, validation, registration, deployment, monitoring, and retraining
• Experience with MLflow, Airflow, Feature Stores, or comparable technologies
• Experience implementing monitoring, logging, alerting, and troubleshooting for production systems
• Understanding of batch scoring and real-time inference architectures
• Experience integrating services with message brokers and analytical data stores
• Experience building AutoML platforms and automating experiments using Optuna or Ray Tune
• Hands-on experience deploying and operating LLMs or building LLMOps infrastructure
• Experience with vLLM, Ollama, LiteLLM, Ray, or similar tools
• Experience managing GPU infrastructure and optimizing LLM inference performance
• Experience with Feast or other Feature Store solutions
• Experience monitoring data drift, concept drift, and ML model performance in production
• The opportunity to work on large-scale products and contribute to their development
• A flexible work environment built on autonomy, ownership, and trust, without micromanagement
• The option to work fully remotely or from a modern office
• An annual $2,000 benefits allowance after the probation period, which can be used for health insurance upgrades, language learning, and sports activities
• Partial reimbursement for professional training, courses, and participation in industry conferences
• Access to a corporate digital library
• Company-funded weekly online sessions with a psychologist
• Relocation opportunities to countries where the company has offices, with relocation assistance available after the probation period, subject to approval