• As an AI & Automation Cloud Infrastructure Engineer, you will shape and constantly develop Jedox's in-house AI cloud platform, allowing expandable, protected, and automated AI, ML, and GenAI workloads. • This highly technical role involves designing, building, and operating robust cloud infrastructures, defining reusable standards and ensuring operational excellence in production. • Design AI cloud solution architectures: Design and develop strong architectures for infrastructure, data, security and integration patterns for AI workloads. • Enable AI, ML and GenAI workloads: Define reusable templates, standards and architectures for efficient cloud-based deployment. • Collaborate across teams: Work closely with the Cloud Platform, SRE, Architecture and Engineering teams to ensure scalable and reliable AI services. • Implement GitOps workflows: Promote controlled environment PR-based processes for models, services and infrastructure. • Support MLOps lifecycle: Contribute to model deployment, promotion, rollback and operational support. • Ensure production readiness: Design for scalability, performance and high availability in production environments, with the aim of ensuring maximum efficiency and reliability. • Strengthen observability, reliability, and security: Enhance monitoring, incident response, and ensure compliance, data protection, and best practices.

Role Description We are looking for a hands-on infrastructure automation engineer to join a 6-month engagement supporting platform modernization and operational continuity at a leading global financial institution. The role is fully remote with required EST working hours (9am–6pm EST). You will lead the migration and modernization of infrastructure automation from Puppet to Ansible, support the stabilization of a critical structured finance platform (Intex/SPG), and help transition the environment toward future integration readiness. This role combines infrastructure automation, operational support, ETL/integration coordination, and structured finance platform support. Qualifications - Ansible — strong hands-on experience (playbooks, roles, inventories, Jinja2 templating) - Puppet-to-Ansible migration experience - Linux/Unix administration - Scripting — Python and/or Bash/Shell - SQL-based environment experience - Familiarity with ETL/data integration workflows and backend pipelines - Full fluency in English Requirements - Design, build, and maintain Ansible playbooks, roles, and inventories for infrastructure automation - Lead the migration of existing Puppet configurations to Ansible - Stabilize and optimize the current Intex platform setup supporting SPG and broader SANCAP processes - Coordinate and support ETL/data integration workflows and backend pipelines - Manage scheduled jobs and batch processing operations - Create and maintain operational documentation to support transition from a single-resource support model - Prepare the environment and processes for future ARDA integration Benefits - Duration: 6 months (July – December 2026) - Location: Remote — full EST hours required (9am–6pm EST) - Client: Leading global financial institution

• Provide services and support as a Midstream Infrastructure Engineer [Independent Engineering (IE) Advisory Services] aligned with the Technical Due Diligence & Engineering Validation For Downstream Oil & Gas / Midstream / Pipelines Functional Area / Swim Lane / Category Discipline in the Energy Industry (Oil, And Gas/Power, And Utilities) Industry Sector focussing on RM | Risk Management Solutions for clients such as U.S. Department of Energy (DOE) | DOE Energy Dominance Financing (EDF) Program. • Provides Independent Engineering advisory support for Energy Dominance Financing (EDF) Program technical due diligence, credit evaluation, lifecycle monitoring, and assurance activities, with emphasis on midstream gathering, processing, compression, storage, and transportation systems. • Reviews project documentation, evaluates technical and commercial interfaces, identifies risks and mitigations, validates assumptions, supports conditions precedent and disbursement readiness reviews where applicable, and prepares defensible work products including facility reviews, flow assurance, capacity checks, equipment evaluations, and operational readiness reports. • Coordinates with engineering, finance, legal, construction, operations, environmental, HSE, and project controls stakeholders to support timely lender and DOE decision-making.

• Build and maintain data pipelines that generate training datasets for machine learning models and experimentation • Contribute to infrastructure that supports distributed training workflows (e.g., PyTorch, Ray) • Work with workflow orchestration tools (e.g., Airflow, Flyte, or similar) to support multi-stage ML pipelines • Improve reproducibility and reliability through dataset validation, monitoring, and testing • Partner with ML engineers to support experimentation and model iteration • Help optimize performance and efficiency across data processing and training systems • Contribute to the evolution of our offline ML platform architecture as it scales