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Build and deploy LLM-based features and agentic workflows for procurement applications. Develop multi-agent frameworks, benchmark models for performance and safety, and create tooling that integrates AI with customer ERP systems. The role involves end-to-end Python development, productionizing AI solutions, collaborating with customers and internal teams, and translating complex technical capabilities into practical solutions.

Build and operate high-volume ingestion pipelines for procurement and supply-chain data. Design schemas, catalogs, warehouses, lakehouses, and feature stores; establish data quality, monitoring, observability, governance, and lineage standards. Collaborate with AI, Platform, and Product teams to provision reliable datasets for analytics and machine learning. Own end-to-end data solutions, CI/CD, integrations, and architecture supporting enterprise customers.

Own end-to-end AI deployments for enterprise customers, from initial scoping through live rollout. Manage senior stakeholder relationships, lead governance, identify additional customer value, and develop repeatable deployment best practices. Translate customer evidence into product and engineering roadmap input while tracking commercial outcomes. The role requires strong delivery or product experience, complex project management, commercial judgment, clear communication, and comfort operating in an ambiguous early-stage environment.

Own the development of user-facing frontend experiences for real-time AI agent workflows. Drive frontend architecture and technical direction, improve code quality, performance, testing, and maintainability, and collaborate with users, Product, Design, and Engineering. Help simplify complex enterprise workflows while shaping interaction patterns for AI-powered software. The role also involves influencing technical decisions in an early-stage, high-ownership environment.

This is an open application invitation: candidates express interest and the employer will contact them when a suitable role becomes available.

Support enterprise sales by qualifying prospects, preparing and delivering demos, drafting follow-ups and RFP responses, maintaining CRM records, coordinating meetings/events, conducting account research, capturing discovery requirements, and relaying customer feedback to product. Gradually take ownership of customer conversations and help improve sales processes and collateral.

Own the full enterprise sales cycle for Magentic's AI agents, building relationships across procurement and supply chain, running discovery with senior stakeholders, managing pilots and negotiations, and partnering with founders, product and engineering to shape GTM and roadmap.

As a Forward Deployed Engineer, you will collaborate with customers to analyze data and shape product development based on insights, while building strong relationships in the manufacturing sector.

As the first Growth Marketer, you'll collaborate with founders to find ideal customers, run growth experiments, and create messaging for Magentic's AI procurement platform.

Design and build scalable backend services and data pipelines in Python, deploy with Docker/Kubernetes, integrate enterprise systems (ERP, APIs, SFTP, event buses), process multi‑terabyte datasets, implement CI/CD and infra-as-code, ensure observability and reliability, and collaborate with customers and ML teams to deliver production enterprise features.