Jobber is hiring a
Senior MLOps Engineer

Summary

Join our team at Jobber as a Senior Machine Learning Operations Engineer and contribute to building an ML platform from the ground up. You will collaborate with Data Scientists and ML engineers, design and implement robust data pipelines, oversee the MLOps lifecycle, and more.

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
• Lead collaboration efforts with Data Scientists and ML engineers to define the scope, requirements, and success criteria for ML projects
• Design and implement robust data pipelines to process raw structured and unstructured data
• 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
• 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

Preferred Qualifications

• Demonstrated experience in building ML platforms or MLOps infrastructure
• Experience with Polars, a high-performance DataFrame library for Rust and Python
• Familiarity with caching tools and strategies for optimizing data access and processing
• Knowledge of vector databases and their applications in machine learning pipelines
• Experience with search engines like Elasticsearch for efficient data indexing and retrieval
• Understanding of ML model serving frameworks and A/B testing methodologies
• Contributions to open-source MLOps tools or frameworks
• Familiarity with ML model versioning tools (e.g., MLflow, DVC)

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
• Stock options
• 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