Coupa makes margins multiply through its community-generated AI and industry-leading total spend management platform for businesses large and small. Coupa AI is informed by trillions of dollars of direct and indirect spend data across a global network of 10M+ buyers and suppliers. We empower you with the ability to predict, prescribe, and automate smarter, more profitable business decisions to improve operating margins. Why join Coupa? 🔹 Pioneering Technology: At Coupa, we're at the forefront of innovation, leveraging the latest technology to empower our customers with greater efficiency and visibility in their spend. 🔹 Collaborative Culture: We value collaboration and teamwork, and our culture is driven by transparency, openness, and a shared commitment to excellence. 🔹 Global Impact: Join a company where your work has a global, measurable impact on our clients, the business, and each other. Learn more on Life at Coupa blog and hear from our employees about their experiences working at Coupa. The Impact of a AI Business Transformation & Design Strategist at Coupa: The AI Business Transformation & Design Strategist is the primary visionary for Coupa’s most strategic AI-led engagements. You are responsible for setting the "North Star" for how a customer’s procurement and finance organization will evolve from human-led manual tasks to Agentic-led orchestration. You are a process architect who thinks in "intents" and "outcomes." You will partner with C-suite stakeholders to identify high-impact workflows where Agentic AI can be deployed, creating the strategic blueprint that the Agentic Architects and Enterprise Solution Managers will then build into reality What You'll Do: 1. Vision Setting & Agentic Strategy - AI Opportunity Mapping: Lead workshops to deconstruct legacy procurement workflows and identify where Coupa Agents (native or custom) can drive the highest ROI. - Agentic Deployment Strategy: Help clients determine the "where" and "how" of agent deployment, deciding which processes should be fully autonomous vs. human-in-the-loop. - Target Operating Model Design: Define the future-state metrics (KPIs) that the AI transformation must achieve, ensuring they align with board-level goals. 2. Readiness & Readiness Assessment - Maturity Audit: Evaluate the customer’s AI Readiness across three lenses: Culture (willingness to trust agents), Data (is the intelligence layer fueled correctly?), and Process (is the workflow "agent-ready"?). - Blocker Mitigation: Identify internal policy or workforce hurdles early and partner with leadership to dismantle them. 3. Change Management & Workforce Evolution - Organizational Change (OCM): Advise on workforce shifts, including the creation of AI Centers of Excellence (COE) and the retraining of staff to manage an "AI Workforce." - Policy Transformation: Guide customers on updating internal governance to allow for autonomous agent actions (e.g., automated contract redlining or tail-spend negotiation). 4. Cross-Functional Leadership - Architect Collaboration: Act as the "Tone Setter" for the technical team. You define the Vision; the Agentic Architect defines the Build. - Roadmap Integration: Bridge the gap between the customer’s long-term transformation goals and the Coupa Product Roadmap to ensure the client is always ahead of the curve. What You Will Bring to Coupa: - Agentic AI Expertise: Deep understanding of the GenAI and Agentic AI landscape (LLMs, RAG, autonomous agents). You must understand what agents can and cannot do today. - Procurement/Finance Domain: Professional fluency in Source-to-Pay (S2P) processes. You need to understand the "Legacy" pain points to architect the "Agentic" solution. - Executive Presence: Possess the dual-fluency to secure C-Suite buy-in on AI vision while simultaneously collaborating with functional leads to map that vision to day-to-day S2P workflows. - Proven Digital Transformation Leadership: 10+ years of experience leading large-scale, cross-functional digital transformation initiatives at the Enterprise level. - Coupa Experience: Previous experience with the Coupa platform is a plus, but we value Agent-First Thinking above platform familiarity. - Consulting Pedigree: Experience in Tier-1 management consulting or leading massive digital transformation projects. - Referenceability: Leading the client through the "25 Compose Engagements" to a state of strong, public advocacy. - Agent Density: Increasing the number of active, high-value agents deployed within a client’s ecosystem. - Executive Impact: Delivery of the Executive Impact Report that proves a measurable shift in operational efficiency. The estimated pay range for this role is $152,000 - $198,000 The starting salary for the successful candidate will be based on permissible, non-discriminatory factors such as skills, experience, and geographic location. Coupa complies with relevant laws and regulations regarding equal opportunity and offers a welcoming and inclusive work environment. Decisions related to hiring, compensation, training, or evaluating performance are made fairly, and we provide equal employment opportunities to all qualified candidates and employees. Please be advised that inquiries or resumes from recruiters will not be accepted. By submitting your application, you acknowledge that you have read Coupa’s Privacy Policy and understand that Coupa receives/collects your application, including your personal data, for the purposes of managing Coupa's ongoing recruitment and placement activities, including for employment purposes in the event of a successful application and for notification of future job opportunities if you did not succeed the first time. You will find more details about how your application is processed, the purposes of processing, and how long we retain your application in our Privacy Policy.

• AI Opportunity Mapping: Lead workshops to deconstruct legacy procurement workflows and identify where Coupa Agents (native or custom) can drive the highest ROI. • Agentic Deployment Strategy: Help clients determine the "where" and "how" of agent deployment, deciding which processes should be fully autonomous vs. human-in-the-loop. • Target Operating Model Design: Define the future-state metrics (KPIs) that the AI transformation must achieve, ensuring they align with board-level goals. • Maturity Audit: Evaluate the customer’s AI Readiness across three lenses: Culture (willingness to trust agents), Data (is the intelligence layer fueled correctly?), and Process (is the workflow "agent-ready"?). • Blocker Mitigation: Identify internal policy or workforce hurdles early and partner with leadership to dismantle them. • Organizational Change (OCM): Advise on workforce shifts, including the creation of AI Centers of Excellence (COE) and the retraining of staff to manage an "AI Workforce." • Policy Transformation: Guide customers on updating internal governance to allow for autonomous agent actions (e.g., automated contract redlining or tail-spend negotiation). • Architect Collaboration: Act as the "Tone Setter" for the technical team. You define the Vision; the Agentic Architect defines the Build. • Roadmap Integration: Bridge the gap between the customer’s long-term transformation goals and the Coupa Product Roadmap to ensure the client is always ahead of the curve.

Santex is a US-based global company founded in 1999, with 26 years of experience in the software industry. Headquartered in California with offices in Córdoba, Argentina, its talent network spans over 18 countries thanks to its flexible, remote-first culture. Santex specializes in custom enterprise software development, operating through Hubs that include eCommerce, BIM, Mobility, Content Delivery, Integration, Web & Mobile Development, Cloud Computing, Artificial Intelligence (AI), Data Science, IT Consulting, and Services. The company is committed to making a positive impact across three dimensions: economic, social, and environmental. Job Description: We are seeking an AI Enablement Engineer to design and scale the semantic layer that turns raw restaurant data into a shared language for AI, analytics, and decision-making across a global Quick Service Restaurant (QSR) enterprise. This is a foundational role in our AI strategy: the semantic layer you build is what makes natural language analytics, automated reporting, and intelligent AI agents possible at enterprise scale. Your focus is bridging business meaning and data, ensuring that AI systems, analytics platforms, and end users consistently interpret critical metrics like same-store sales, speed of service, traffic, and labor productivity in a standardized, governed way. When an AI agent or a franchisee asks "how did my restaurant perform last week," the accuracy and trust of that answer starts with your work. You will define and operationalize a hybrid semantic layer strategy that combines governed, metadata-driven models with AI-powered context engineering to enable scalable, accurate, and self-service insights across restaurants, corporate teams, and franchisees, unlocking the building blocks for conversational analytics and autonomous AI workflows. Key Responsibilities – Semantic Layer Strategy & Architecture - Define and implement an enterprise semantic layer that standardizes metric definitions and aligns business questions to data - Establish canonical definitions for core KPIs (e.g., same-store sales, speed of service, traffic, labor productivity) - Design mapping between business concepts and underlying data models across systems (POS, digital, supply chain, finance) - Provide structured metadata to power AI systems, copilots, and analytics tools. Key Responsibilities – Hybrid Semantic Model Implementation - Build and maintain manual, metadata-driven semantic models for high-criticality domains (finance, core KPIs, franchise reporting) - Enable AI-driven context engineering for exploratory analytics, operational insights, and ad-hoc queries - Define how both approaches coexist in a unified architecture Key Responsibilities – Tooling & Platform Enablement - Implement semantic layer tooling (DBT, semantic views, YAML/Markdown metadata, data catalogs like Atlan) - Enable downstream consumption by BI tools, APIs, and AI applications - Create reusable abstractions and templates to scale globally Key Responsibilities – AI & Context Engineering Integration - Enable AI systems (LLMs, agents, copilots) to leverage semantic metadata - Support context engineering using data lineage, metadata, example queries, and repository context - Improve AI reliability by grounding outputs in governed definitions Key Responsibilities – Governance & Data Quality - Establish governance processes for metric definitions - Ensure alignment across regions, brands, and franchise reporting - Implement validation, versioning, and change management - Define guardrails to prevent inaccurate analytics outputs Key Responsibilities – Adoption & Enablement - Partner with business and technical teams to drive adoption - Educate stakeholders on standardized metrics and self-service analytics - Provide documentation, playbooks, and training Required Qualifications - Bachelor’s or Master’s degree in Computer Science, Data Engineering, Analytics, or related field - 5+ years of experience in data engineering, analytics engineering, or AI/ML systems - Strong experience with semantic modeling or analytics engineering - Proficiency in SQL and Python - Experience with DBT and modern cloud data platforms - Understanding of APIs and distributed systems Preferred Qualifications - Experience in QSR, retail, or franchise environments - Experience with semantic/metrics layers - Familiarity with data catalogs (e.g., Atlan) - Experience with generative AI, LLMs, and context engineering - Knowledge of data lineage and governance frameworks Additional Expectations - Design and maintain CI/CD pipelines, including automated builds, testing, and deployment of solutions - Develop and execute functional and regression test suites to validate metric accuracy, data completeness, and semantic model behavior across environments - Implement automated data and code quality checks and integration tests within the deployment pipeline to catch issues before they reach production - Apply software engineering best practices including version control, code review, branching strategies, and environment promotion (dev/staging/prod) to all semantic layer and metadata assets - Monitor platform performance, troubleshoot pipeline failures, and maintain observability across semantic layer infrastructure using logging, alerting, and health checks - Define and manage infrastructure as code (e.g., Terraform, CloudFormation) for data platform resources, ensuring reproducible and auditable environment provisioning across development, staging, and production - Implement and maintain role-based access controls, secrets management, and security policies across semantic layer tooling, data platform resources, and API integrations to ensure compliance with enterprise security standards Key Skills & Competencies - Systems thinking across data, analytics, and AI - Ability to translate business metrics into data models - Balance governance with scalability - Strong stakeholder management - Clear communication of complex concepts Success Metrics - Adoption of standardized metric definitions - Reduction in reporting inconsistencies - Improved accuracy of AI-generated insights - Increased self-service analytics usage - Faster time-to-insight Reporting Structure - Reports to: VP / Director of Data, AI, or Platform Engineering - Works closely with: Data Engineering, Analytics, AI/ML, Finance, Operations, Franchise teams Summary The AI Enablement Engineer (Semantic Layer) establishes a shared language between business and data. By combining governed semantic models with AI-driven context understanding, this role enables scalable, accurate, and trusted analytics and AI across a global QSR enterprise. Location: LATAM

Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. Tenstorrent is seeking an AI Performance Simulation Architect to help shape the future of our AI computing. In this role, you’ll architect and build scalable cycle-accurate AI accelerator performance models that can run representative AI workloads. Your work will directly inform how we design and optimize future hardware platforms, from high-level performance models to cycle-accurate simulations. This role is remote, based out of North America. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting. Who You Are - You’re a senior technical leader with deep experience in AI accelerator architecture and micro-architecture, performance simulation, and system-level modeling. - You’re comfortable switching between abstraction layers—from C++ performance models to software frameworks and system requirements. - You have a strong grasp of AI workloads and how to distill them into inputs for performance models. - You understand how system design affects the performance and scalability of AI workloads. - You collaborate well across hardware, software, and research domains—and enjoy mentoring others along the way. What We Need - Architect and develop simulation environments for AI systems—spanning accelerators, NoCs, and memory hierarchies. - Define and implement abstraction layers, from high-level analytical models to detailed simulations using tools like GEM5 or SST. - Lead performance modeling and bottleneck analysis to guide architectural design and system optimization. - Lead integration of the AI accelerator performance model into a larger heterogeneous simulation modeling environment to examine system performance. - Deep experience with parallel programming (OpenMP, MPI, CUDA, OpenCL) and system analysis tools. What You Will Learn - How Tenstorrent is building next-gen AI infrastructure at datacenter scale. - How to influence silicon and system-level decisions using simulation-driven insights. - How to collaborate with vendors and technology partners to assess emerging processors, accelerators, and interconnects. - How to bridge simulation and deployment by working across hardware, dev tools, software stacks, and customer use cases. Compensation for all engineers at Tenstorrent ranges from $100k - $500k including base and variable compensation targets. Experience, skills, education, background and location all impact the actual offer made. Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer. This offer of employment is contingent upon the applicant being eligible to access U.S. export-controlled technology. Due to U.S. export laws, including those codified in the U.S. Export Administration Regulations (EAR), the Company is required to ensure compliance with these laws when transferring technology to nationals of certain countries (such as EAR Country Groups D:1, E1, and E2). These requirements apply to persons located in the U.S. and all countries outside the U.S. As the position offered will have direct and/or indirect access to information, systems, or technologies subject to these laws, the offer may be contingent upon your citizenship/permanent residency status or ability to obtain prior license approval from the U.S. Commerce Department or applicable federal agency. If employment is not possible due to U.S. export laws, any offer of employment will be rescinded.