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 chance 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

📌 MLOps Engineer IT (Victoria)
🏢 01.tech
📍 Victoria