The primary owner of IT automations that keep operations running, building quick wins, and moving IT configuration, infrastructure, and identity into code.
Locations (2)
United States
Chicago, Illinois, United States
Compensation
Base salary range $142,718 $161,904 annually. All full-time roles are eligible for an equity package in the form of stock options.
Responsibilities
Owning onboarding, termination, and device logistics automation end to end
Running the IT Workato workspace, including recipes, connections, error handling, and alerts
Building the safety net: runbooks, monitoring, and a clear failure response for IT flows
Building workflows in Okta and Rovo/Jira that remove manual steps from IT processes
Laying the foundation for IT as code: a repository, cloud-hosted in-house tooling, and Terraform, CI/CD, and linters, with Python and Bash to tie it together
Senior Risk Modelling & Analytics Manager to help shape risk strategy and build models and analytics for Jobber's Fintech business, focusing on credit and fraud risk.
Locations (4)
Edmonton, Alberta, Canada
Toronto, Ontario, Canada
Vancouver, British Columbia, Canada
Kitchener-Waterloo, Ontario, Canada
Compensation
This role has a minimum annual salary of $145,860, a midpoint of $171,600, and a maximum salary of $197,430
Responsibilities
Build and implement quantitative risk models end to end in Python (credit risk scorecards, PD, loss, fraud detection)
Lead and develop a high-performing database administration team, drive enterprise database strategy and modernization, ensure reliability, performance, and operational excellence, manage service delivery and resource planning, advance automation and DevOps practices, strengthen architecture and security, and drive continuous improvement.
Responsibilities
Lead and develop a high-performing database administration team
Drive enterprise database strategy and modernization
Ensure database reliability, performance, and operational excellence
Manage service delivery, resource planning, and operational priorities
Advance automation, DevOps, and database engineering practices
Requirements
Bachelor's degree in Computer Science, Information Technology, or a related field
Nice to have: Experience managing database services across multiple public cloud platforms
Senior Software Engineer to join the Case Intelligence team, building Radar and MRR to turn court records, expert reports, depositions, and medical records into usable information for attorneys. This is a backend and data engineering role with a strong applied AI component.
Responsibilities
Design, build, and operate pipelines using AWS services such as SQS, Lambda, ECS/Fargate, Step Functions, S3, and EventBridge.
Turn large volumes of unstructured documents into reliable, structured data.
Build production LLM workflows using Amazon Bedrock, OpenAI, and Gemini, including batch inference, structured outputs, model routing, and long-document processing.
Develop retrieval and ML components using embeddings, OpenSearch, reranking, classifiers, and entity resolution where they improve results.
Work with evaluation team to turn QA findings into measurable improvements in output quality.
Liderar a estratégia de uso e adoção de Inteligência Artificial nos produtos de Marketing Services, conectando profundidade técnica em GenAI com oportunidades concretas de produto, eficiência operacional e geração de valor para a BU.
Responsibilities
Conduzir experimentos comparativos com diferentes LLMs, agentes e arquiteturas de GenAI aplicados a casos reais de negócio
Projetar, otimizar e evoluir pipelines de RAG, incluindo estratégias de chunking, embeddings, retrieval e avaliação contínua
Estruturar frameworks de avaliação e benchmarking para aplicações GenAI, considerando qualidade, relevância, confiabilidade, custo, latência e aderência ao caso de uso
Medir performance, custo e ROI de soluções baseadas em IA, sustentando decisões de priorização e escala
Decidir quando utilizar Prompt Engineering, RAG, Fine-Tuning ou arquiteturas multiagentes, respondendo pelos trade-offs técnicos e de negócio da escolha
Design, develop, and maintain scalable data ingestion, transformation, and ETL/ELT pipelines; build and optimize Azure-based data platforms; support deployment and operationalization of machine learning models; implement monitoring, logging, troubleshooting, and performance optimization; collaborate with data scientists and engineering teams; develop reusable, automated components following cloud engineering best practices.
Responsibilities
Design, develop, and maintain scalable data ingestion, transformation, and ETL/ELT pipelines.
Build and optimize Azure-based data platforms using Databricks, Data Lake, Synapse, and related technologies.
Support deployment and operationalization of machine learning models through CI/CD and MLOps practices.
Implement monitoring, logging, troubleshooting, and performance optimization for data and ML workloads.
Collaborate with data scientists and engineering teams to translate requirements into production-ready solutions.
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