Jobber is hiring a
Senior MLOps Engineer

Summary

Join our team as a Senior Machine Learning Operations Engineer and build an ML platform from the ground up to unlock improved operational outcomes, workflow efficiencies, and new business insights across our organization.

Requirements

• A background in software or data engineering
• Polished communication skills with a proven record of leading work across disciplines
• Strong proficiency in Python programming
• Extensive experience with Apache Spark for large-scale data processing
• Expertise in containerization, particularly Docker and CI/CD technologies
• Experience designing and implementing RESTful APIs
• Comprehensive knowledge of AWS services, including: ECS Fargate for container orchestration, EMR (Elastic MapReduce) for big data processing and AWS Glue for ETL workflows
• Proven track record of building and maintaining complex ETL pipelines
• Experience with workflow management tools, specifically Apache Airflow
• Proficiency in using dbt (data build tool) for data transformation and modelling
• Strong understanding of DevOps principles and CI/CD practices
• Excellent problem-solving skills and attention to detail
• Ability to work effectively in a fast-paced, collaborative environment

Responsibilities

• Collaborate in architecting and building a comprehensive ML Platform from the ground up, enabling Data Scientists and ML engineers to efficiently develop, deploy, and manage ML models
• Lead collaboration efforts with Data Scientists and ML engineers to define the scope, requirements, and success criteria for ML projects, ensuring alignment with business objectives
• Design and implement robust data pipelines to process raw structured and unstructured data, proactively building features for feature stores to support diverse ML use cases
• Oversee the complete MLOps lifecycle, including requirements gathering, data cleaning and organization, model development, production deployment, monitoring, and maintenance
• Conduct thorough feasibility analyses through proofs-of-concept (POCs) and provide data-driven recommendations on preferred approaches, tools, and products within the open-source MLOps ecosystem
• Develop and maintain a deep understanding of Large Language Models (LLMs) and their specific MLOps requirements, staying current with rapid advancements in this field
• Implement and optimize end-to-end MLOps pipelines for model training, evaluation, and deployment, ensuring scalability and efficiency
• Establish and implement best practices for version control, testing, and monitoring of ML models, promoting reproducibility and reliability
• Architect scalable and efficient data processing systems capable of handling large-scale machine learning applications
• Continuously assess and improve the MLOps infrastructure to enhance performance, reliability, and cost-effectiveness

Benefits

• A total compensation package that includes an extended health benefits package with fully paid premiums for both body and mind, retirement savings plan matching, and stock options
• A dedicated Talent Development function, including Development Coaches, to help build the career you want and hit the goals you set, while ensuring you’re reaching your fullest potential
• Support for all. your breaks: from vacation to rest and recharge, your birthday off to celebrate, health days to support your physical and mental health, and parental leave top-ups to support your growing family