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 workplace 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
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📌 Mlops Engineer It & Data Development (Victoria)
🏢 01.tech
📍 Victoria